LIBRARY Michigan State University This is to certify that the thesis entitled METRIC METHODS USED TO DETERMINE RACE: CAN THEY IDENTIFY NATIVE MICHIGAN POPULATIONS? presented by Shirliejean Raven Arnold has been accepted towards fulfillment of the requirements for the MS. degree in Forensic Science Ma'jor Professor’s Signature 407 “M _, fir V Date MSU is an Affirmative Action/Equal Opportunity Institution ...- —-----C-I—.-c—.-.--o—.-o-o—o-O-i-O-O-O-n-C-0-0-.-.-‘gfiu—t . PLACE IN RETURN BOX to remove this checkout from your record. To AVOID FINES return on or before date due. MAY BE RECALLED with earlier due date if requested. DATE DUE DATE DUE DATE DUE * ‘ 2/05 c:/ClRC/DateDue.indd.p.15 METRIC METHODS USED TO DETERMINE RACE: CAN THEY IDENTIFY NATIVE MICHIGAN POPULATIONS? By: Shirliejean Raven Arnold A THESIS Submitted to Michigan State University In partial fulfillment of the requirements For the degree of MASTER OF SCIENCE Department of Criminal Justice 2005 ABSTRACT METRIC METHODS USED To DETERMINE RACE: CAN THEY IDENTIFY NATIVE MICHIGAN POPULATIONS? By Shirliej can Raven Arnold The existence and definition of race are debated in modern anthropology, but forensic anthropologists must make racial estimations when creating biological profiles of found skeletons. Researchers must categorize a skeleton as White, Black, or Asian/Native American. The options for distinguishing between White and Native American remains are few and the remains used to create them are biased toward Southwest and Great Plains tribes. This creates the potential for incorrect estimations in other areas of the United States because genetic, temporal, and social differences exist between different tribes. This study tests the Giles and Elliot (1962), Gill et a1. (1988), and Fordisc 2.0 (Jantz and Owsley, 1996) methods for assessing race. Eighty-one Native American crania from the University of Michigan osteological collection were measured for race determination. Fordisc 2.0 was 90% correct, Giles and Elliot was 68% correct, and Gill et al. was 67% correct in race estimation. All three methods produced significant error in race estimation based on z-scores, but Fordisc 2.0 was the most successful. This study has shown that Michigan Native Americans vary significantly from Southwestern and Great Plains Native Americans in their cranial form and these three methods for determining race are not ideal for use in this population. ACKNOWLEDGEMENTS This work would not be possible without the support and help of some special people. Thanks are due to Norm Sauer, my thesis chair, who provided plenty of useful feedback on each draft. Special thanks to Karen O’Brien who was a helpful, gracious host at the University of Michigan human osteology collection. Christina DeJong offered advice and statistical expertise that proved invaluable. Thanks to Mary Megyesi for her careful, meticulous readings and reworkings of the many drafts. Thanks also to my husband, Jonathan Arnold for his assistance with data collection and his editing skills. iii Table of Contents List of Tables ......................................................................................... v List of Figures ....................................................................................... vi Introduction: Race in Anthropology .............................................................. 1 Ancestry Assessment in Native American Remains: A Review .............................. 7 Materials and Methods................................ .............................................. 14 Results ................................................................................................ 19 Discussion ............................................................................................ 24 Conclusion ........................................................................................... 26 Appendices .................. ' ........................................................................ 29 Literature Cited ............................... _ ...................................................... 49 iv List of Tables Table 1. Measurements and Definitions.............................. ....................... 15-16 Table 2. Giles and Elliot Forumulae ......................................................... 17 List of Figures Figure 1. Actual Sexes of the Sample ...................................................... 19 Figure 2. Fordisc 2.0 Results .. ...................................... ' ......................... 20 Figure 3. Giles and Elliot Results ........................................................... 21 vi Introduction: Race in Anthropology There is much debate among anthropologists today about the concept and definition of race. Some believe that human beings can be divided into races based on physical characteristics and innate capabilities (Sarich and Miele, 2004). Others argue that this is impossible since physical traits correlate neither with each other nor with innate abilities (Livingstone, 1964; Brace, 1996; Washburn, 1963). ’Montagu quotes Washbum as saying, “it is impossible to consider each of the two billion persons in the world. Therefore some system of sampling is necessary. It happens that mankind does divide into great groups so that a relatively small number of individuals may substitute for the entire group . . .The racial classification is a simple sample system which allows a student to become familiar with the superficial characters of two billion people in a remarkably short period of time” (Montagu, 1963, pg. 29). This is an example of the position that perhaps “races” exist but more importantly some form of categorization is necessary. This debate has led to the calling of some anthropologists for disuse of the term “race” with all of its hidden meanings and baggage. Sauer (1992) instead suggests using “ancestry” as a term simply denoting a person’s likely geographic origins. He says, “No one who argues against the race concept denies that human variation exists or claims that this variation is not systematic. In fact, it is systematic variation that allows anyone to estimate, with varying degrees of specificity a person’s place of ancestry from their physical features” (Sauer, 1992, pg 110). Both sides agree, though, that race is a social construction with massive sway in the public eye (Lieberman et a1., 1989). It is therefore understandable why forensic anthropologists regularly deal with race as a part of a biological profile. When presented with a body to identify, the forensic anthropologist must construct a biological profile including the age, sex, stature, and race of the person. This profile allows police to better match the remains with missing persons reports. When dealing with human remains in the public sector, attention must be paid to identifying race because it is a large part of a person’s social identity and to work without it would be to work severely disadvantaged. Since race is such a crucial aspect of a person’s social identity, a forensic anthropologist normally offers some information to this regard (Brace, 1995; Kennedy, 1995; Sauer, 1992). Students of forensic anthropology must “appreciate the paradox of how a scientific approach to the study of human evolution and biological diversity co-exists with a non—scientific belief in the existence of human ‘races’ in the context of determining ancestry in a forensic anthropological investigation and reporting the results of the study in records submitted to clients from medical-legal agencies” (Kennedy, 1995, pg.800) To assign a racial category to a skeleton, a forensic anthropologist must'first determine the skeleton’s region of origin. To this end, the forensic anthropologist may use metric or non-metric means. When using metric means, different aspects of the skeleton are measured. Measurements are entered into functions or plotted on predetermined graphs and the skeleton is categorized into a racial group based on its association with other skeletons. One example of a metric analysis technique used to separate the races is the index. This is the relationship between two measurments. For example, the cranial index is the cranial breadth divided by the length (Downs and Bleibtreu, 1969). Non-metric (or morphological) methods are those that assign regional origin based on examination of the skeletal features. For example, shovel shaped incisors are often a sign of Asian decent. If they are present in a skull, the skull is likely Asian/Native American (Hinkes, 1990). Both metric and non-metric methods are commonly used in anthropology to estimate the race of individuals in both a forensic and bioarchaeological context. Metric and non-metric methods of determining race are both susceptible to genetic and environmental influences. Researchers should be aware of the populations with which these methods and equations were tested and created. Using these methods on individuals or groups outside the sample population could introduce unknown error into the estimate. For instance, when working with a native population from the southeastern United States, a researcher should consider the possible problems with using a technique created using Eskimo remains exclusively. Environmental, chronological, and social differences between the two groups could potentially introduce error into ancestry estimates that rely on variable cranial and racial skeletal structure. Regardless of whether metric or non-metric means of evaluation are used to determine race, it is important to understand where and from which populations the techniques were derived. The three most commonly used racial categories in forensic anthropology in the US. today are White/European, Black/African, and Asian, or, in more anachronistic terms, Caucasoid, Negroid, and Mongoloid (Bass, 1995; Brace, 1996; Downs and Bleibtreu, 1969). The goal of the forensic anthropologist is to be able to identify human remains, which includes categorizing them into one of these three races based on the skeleton alone. Most scientific attention has focused on being able to distinguish between White and Black groups, as these are the two primary groups in the American population (75.1% White and 12.3% Black in 2000 according to the United States Census Bureau). However, limited work has also been done with Asian populations, primarily on Native American remains. Despite the fact that only 0.9% of the United States’ population was comprised of Native Americans in 2000 (United States Census Bureau), there is still much value in research on identifying their remains. It is less surprising when one learns that in some regions of the United States the majority of forensic anthropology cases involve Native American remains. This is because ancient remains are often uncovered during roadwork and construction. Cases such as these are forensic in two ways. First, the remains must be assumed to be modern and of legal import until proven otherwise. Thus, every case is forensic until age or Circumstance shows it to be beyond legal Significance. Secondly, the Native American Graves and Repatriation Act of 1990 requires that all Native American remains must be repatriated to the appropriate tribe, if possible, and so must be officially associated with a particular tribal entity. A connection between the remains and a modern nationally recognized tribe must be established by physical, historical, and/or cultural means (Native American Graves and Repatriation Act, 1990). This makes Native American remains important to forensic anthropologists because forensic methods of ancestry assessment can be used on these remains to help identify past relationships and population movements among Native groups. They also help identify relationships between groups of the past and groups of the present. These are the two largest reasons for forensic concern with Native American remains. Consequently, sufficient research and diverse means of analysis must be available to distinguish Native American remains from other remains of non-Native origin. Repatriation’is difficult because remains must be associated with a modern tribe by means of comparison with ancient populations. Forensic anthropologists must distinguish between ancestral populations based on the skeleton and associated cultural markers. This is a finer distinction than between races since all Native Americans are considered to belong to the same race. There are countless regional, cultural, and temporal differences between Native American groups. Environmental conditions can affect the bodies of individuals in populations that have lived there for a long time and adapted to the climate (Lahr, 1996; Dolhinow and Sarich, 1971; Downs and Bleibtreu, 1969; Corcos, 1997; Molnar, 1975, 2002; Sarich and Miele, 2004). Environmental factors have been shown to have great influence on bodily form in a variety of studies (Corcos, 1997; Downs and Bleibtreu, 1969; Halloway, 2002; Molnar, 1975, 2002; Sarich and Miele, 2004; Sparks and J antz, 2002; Steward, 1945). Once environment-specific traits become dominant in a population, the genetic spread of such traits to other populations can be limited by geographic and social filters. Social taboos and marriage customs can be as imposing as physical partitions like mountains and deserts, preventing the free exchange of genes. The frequency of certain traits, then, would vary between even neighboring places (Downs and Bleibtreu, 1969). For instance, the Hopi Indians are on average ten to twelve centimeters taller than the Papago, a tribe living only 200 miles to the south (Molnar, 2002). Brace (1995) argues that not all physical traits are necessarily adaptive and are the product of genetic drift, social and physical restrictions, and other such genetic restrictions. “Regional clusters of populations then owe the similarities in their appearance to the perpetuation of traits that are shared by virtue of kinship but which have no other biological significance” (Brace, 1995, pg 173). Considering these views on the spread of genetically defined variation, it is not difficult to accept that the skeletal morphology of different tribes and populations could be different from area to area within the United States. Ancestry Assessment in Native American Remains: A Review . Since research on determining Native American identity is rarer than the other forms of ancestry research, there are few methods available. The methods that have been developed to identify Native American remains are frequently only successful when performed by the developers and then only when tested on certain populations. I These methods have not been widely tested and few forensic anthropologists have validated them on their local populations. It is important to make certain that environmental and genetic differences have not changed a local population in such a way as to make predictive methods developed elsewhere useless. Thus, forensic anthropologists should test their common methodologies on a local population prior to a career of usage. There are two major collections that are used frequently to develop the analytical methods for determining race. These are the Hamann-Todd Collection, located in Cleveland, Ohio, and the Terry Collection, located at the Smithsonian in Washington DC. Both were begun in the late 18003 and represent adults who lived from the late 1800s to the mid 19005. These collections are large (over 1,000 skeletons in each) and information about age, sex, ancestry, and stature is known for most individuals (Cleveland Museum of Natural History, 2002; Hunt, 2004). The Hamann-Todd and Terry collections are some of the best resources for developing and testing ancestry methods because they offer a large selection of remains often of known life histories. The remains in the Hamann-Todd and Terry collections are the foundation for many metric and non-metric techniques for determining ancestry as well as other aspects of a biological profile. However, most ancestry techniques derived from these populations are heavily biased toward White and Black populations. There is a Native American component in the Hamann-Todd collection but it is very small since the remains for this collection were taken primarily from the unclaimed bodies at the city morgue. Techniques for ancestral identification based on or tested only on the Terry Collection are not representative of, and cannot properly assess, Native American remains. Nevertheless, many of the different methods for assessing the race of human ' remains have been developed from these collections. Based on work from both collections, the most widely known and used metric analysis for distinguishing between populations based on ancestry was authored by Giles and Elliot (1962). Its popularity stems from its claim to distinguish between Whites, Blacks, and Native Americans (as well as the sexes) while requiring only eight measurements. It was developed using the Terry collection, the Todd collection, and a Native American sample drawn exclusively from the Archaic Indian Knoll site in Kentucky. Giles and Elliot used 408 specimens composed of 108 White males, 79 White females, 113 Black males, and 108 Black females. The number of Native American specimens used was not published. This method requires that the eight measurements be entered into a series of formulae, creating a discriminant fimction, which will produce a figure representing a racial affinity. Each measurement is weighted with predetermined weights to produce an affinity score. The Giles and Elliot (1962) discriminant functions were tested by Fisher‘and Gill (1990) on a mixed Northwestern Plains Indian sample from the University of Wyoming skeletal collection. Twenty-seven skeletons of whose sex and ancestry had already been determined by anthroposcopic methods and archaeological context were analyzed using the Giles-Elliot system. The results suggested that the Giles and Elliot system was only 25% accurate in race estimation. The authors performing the comparison claimed that there was more morphological variation among Native American groups than the Giles- Elliot analyses allow. Only a small percentage of all Native American variation was represented by those remains found at Indian Knoll. Fisher and Gill concluded that metric analyses should not extend beyond the population used to derive them. This conclusion is also supported by Ayers et a1. (1990). Ayers et a1. tested the Giles-Elliot functions using 191 forensic cases, 11 of which were Native American, where sex and ancestry could be accurately ascertained from soft tissues. They found similar results as Fisher and Gill (1990). Many of the Native American remains were improperly classified. The authors conclude that the Giles and Elliot (1962) discriminant function analyses are only useful within the population used to create them. When Iscan (1990) tested the Hamann-Todd collection and the Terry collection, he found Giles and Elliot to be around 95% successful, though this test was primarily done only between White and Black crania. It was also a test of the same collections used to create the function. Iscan (1990) warns against using these formulae outside the originating population. A series of indices was developed for distinguishing between Black, White, and Amerindian remains by Gill et al. (1988). The authors developed this method using 125 white crania from the Terry Collection, Smithsonian Institution, and forensic cases, as well as 173 Native American remains from the Arikara, Pawnee, Dakota, northwestern Plains, Omaha, Minnesota, and Mimbres tribes. A modified coordinate caliper called a simometer was used to test the utility of 14 measurements. In the end, three indices were created from six measurements that separated white from Native American skulls. They were the maxillofrontal index, zygoorbital index, and alpha index. For each, a measurement called the subtense (basically, the projection of the nasal bones at a given breadth) was divided by the breadth, then multiplied by 100 for a percentage relationship between the subtense and breadth. Sectioning points were set at 40-38-60 for the three indices respectively meaning that scores below these numbers were considered Native American. Gill et al. had more than 90% accuracy classifying Native Americans and Whites regardless of bone condition and found that this method could be more accurate than visual methods alone. The authors claimed features of the mid-face are under strong genetic control and are little affected by the environment (Gill et al, 1988). In 1990, Gill and Gilbert tested the method outlined by Gill et a1 (1988). The fourteen measurements were taken of the skulls of 398 of individuals. The sample consisted of 125 Whites, 100 Blacks, and 173 Native Arnericans. The number of measurements used was again dropped to 6, forming the same three indices. Using these indices, the authors were able to correctly assess the ancestry of the skulls between 87.0% and 88.8% of the time. Gill et al.’s 1988 method seems to be a promising method for distinguishing between the remains of Whites from Blacks and Native Americans, but not between Blacks and Native Americans, based on the accuracy of the original study and subsequent tests (Gill et al., 1988; Gill and Gilbert, 1990). Dr. Richard L. Jantz and Dr. Stephen D. Ousley (1996) of the University of Tennessee, Knoxville (UTK) designed a software program that classifies skeletons into ancestry groups. A forensic skeletal sample called the Forensic Data Bank was created using skeletal measurements from forensic cases at UTK and from other forensic cases around the country whose measurements were submitted to UTK. Using the Forensic 10 Data Bank, they created Fordisc 2.0, a computer program that uses discriminant function analysis on cranial and post-cranial measurements to determine ancestry and sex. Between 1 and 34 measurements are required to allot a cranium to one of several ’ different ancestry groups. The Native American sample is composed mostly of Southwest and Great Plains tribes though there has been input from other places as well. The accuracy rates offered by the creators are 95% correct ancestral differentiation between Black and White remains and 96% between Black, White, Native American, and Chinese (Owsly and J antz, 1996). This program is used by many forensic anthropologists because of its versatility, ease of use, and accuracy (Ubelaker, 1998). In 2002, Ubelaker et al. reported on an application of Fordisc 2.0 to a sample of Spanish crania. They found that 44% were classified as White, 35% as Black, 9% Hispanic, 4% American Indian. The remaining were classified as Chinese or Vietnamese. The authors explain these results in two ways. First, the crania were noticeably small and could therefore have “fooled” the computer. Second, and most importantly, F ordisc 2.0 does not have a category that would have been a perfect fit for a Spanish sample. The remains used to create the system were American forensic cases meaning that samples from elsewhere in the world could not be classified correctly. The program simply lacks the appropriate database from which to draw a conclusion. In their final words, however, the authors suggest that a global database would only improve “an already useful forensic tool” (Ubelaker et al., 2002, pg 4). Fordisc 2.0 was tested again in 2005 by Williams et al. This time the sample was from ancient Nubia and the program did not perform well. The results ranged through all of the available categories leading the authors to argue that F ordisc 2.0 is based on a poor 11 database. Further, they claim it perpetuates the improper methods for separating groups of people into races based on stereotypical features while disregarding subtleties in variation (Williams et al., 2005). As this short review demonstrates, there has been little research on differentiating between Native Americans and other racial groups. In fact, there are only a few metric analyses available that examine Native American remains and these are biased toward the Southwest and Plains Indian tribes. Of those choices, it is also obvious that the success rates for these are not exceptional, Giles and Elliot being found to be only 25% accurate in one instance (Fisher and Gill, 1990). Many authors of validation studies regarding these metric methods believe that the poor success rates are due to the differences in Native American sample populations (Fisher and Gill, 1990;Ayers et al., 1990; Ubelaker et al., 2002). Samples tested from collections outside the home range of the method rarely work well. If the commonly held methods for distinguishing Native American from White and Black remains have not been shown to be very successful outside of the original sample population, their utility in other areas of the country ought to be examined. Each area could have its own unique skeletal adaptations to the environment and history of ancestry. Trait frequencies could be unaccounted for in methods developed in other areas. Hence, areas beyond the bounds of the original sampling of a technique should be tested before use and reliance. Without such testing, there is the potential for inaccuracy in forensic evaluations. This study proposes to assess the utility of three methods for distinguishing between White and Native American populations from the Great Lakes region, 12 specifically Michigan. This study will address the Giles and Elliot (1962), Gill et al. (1988), and Fordisc 2.0 metric methods for utility in distinguishing between Native American and White populations. This is important because the Great Lakes sample was not one used to formulate any of these methods and could thus prove problematic. There could be physical and/or genetic differences between the populations used to create the methods and the Great Lakes population. I believe that F ordisc 2.0 program will work well at determining‘the race of my sample. I do not think the Giles and Elliot (1962) or Gill et al. (1988) methods will be successful because of the restricted samples used to create them. 13 Materials and Methods The 81 crania used for this study are housed in the human osteology collections held at the University of Michigan in Ann Arbor, Michigan. All of the remains used were recovered from archaeological excavations conducted in Lapeer (10 skulls), Clinton (3), Macomb (1), Cass (l), Menominee (6), Braunch (2), Benzie (2), Missaukee (6), Bay (1), Marquette (2), Antrim (1), Leelanau (1), Alcona(1), Lake (1), Saginaw (4), Oakland (3), St. Clair (1), Jackson (1), Tuscola (4), Isabella (l), Sanilac (1), Huron (1), Presque Isle (2), Gratiot (1), Montrnorency ( 1), Washtenaw (13), Ottawa (1), Wayne (5), Otsego (1), and Emmet (1) counties of Michigan. Based on contextual and scientific evidence, all remains were judged to be prehistoric, varying from Early to Late Woodland, and therefore of Native American descent. The skulls varied in Completeness though only those with most osteological landmarks available were used. They were measured by the author in the spring of 2005. For the Giles and Elliot and F ordisc 2.0 techniques, measurements were taken with standard sliding and spreading calipers. These measurements can be found in Table 1. All measurement definitions were taken from Buikstra and Ubelaker (1994) and all point definitions were taken from Bass (1995) and White (2000). The measurements for the Gill technique were taken from the Gill et al. (1988) and Gill and Gilbert (1990) articles and were performed using a simometer. These are also listed in Table 1. Point definitions were again taken from Bass (1995) and White (2000). 14 Table 1. Measurements and Definitions the naso-maxillary suture meets the nasal aperture maximum cranial length glabella to opisthocranion G&E, Fordisc maximum cranial breadth eurion to eruion G&E, Fordisc bizygomatic diameter 3:12:22“ distance between zygomatrc G&E, Fordisc basion-bregma height basion to bregma G&E, Fordisc cranial base length basion to nasion G&E, Fordisc basion-prosthion length basion to prosthion G&E, F ordisc maxillo-alveolar breadth ectomolare to ectomolare Fordisc maxillo-alveolar length prosthion to alveolon Fordisc biauricular breadth auriculare to auriculare Fordisc upper facial height nasion to prosthion G&E, F ordisc minimum frontal breadth frontotemporale to frontotemporale ordisc upper facial breadth frontomalare temporale to frontomalare For disc temporale basal height nasion to nasospinale Fordisc nasal breadth maximum breadth of nasal aperture G&E, Fordisc orbital breadth dacryon to ectoconcion ' Fordisc brbital height distance from most superior and inferior For disc ornts on orbital margrn biorbital breadth ectoconchion to ectoconchion Fordisc interorbital breadth dacryon to dacryon Fordisc frontal chord nasion to bregma Fordisc parietal chord bregma to lambda Fordisc occipital chord lambda to opisthion F ordisc foramen magnum length basion to opisthion Fordisc foramen magnum breadth distance between most lateral margins of For disc foramen magnum mastoid length ertical projection of mastoid process F ordisc maxillofrontal breadth maxillofrontale to maxillofrontale Gill et al. naso-maxillofrontal subtensegfilzitgefmm maxrllofrontal breadth to nasal Gill et al. mid-orbital breadth zygoorbitale to zygoorbitale Gill et al. naso-zygoorbitale subtense distance. from zygoorbrtale breadth to Gill et al. nasal brrdge breadth from points left and right along 3 alpha chord lrne from zygoorbitale to the pornt where Gill e t al. 15 Table 1. Measurements and Definitions (cont) aso-alpha projection from the apha points ot the deepest point ubtense on the nasal bridge GI“ et al. To test F ordisc 2.0, the measurements were entered into the computer program that performed all necessary calculations. The results were displayed as a series of probability scores and a graph of the proximity of the skull in question to the average scores of each of the target groups. The posterior probability score was of use here because it is the score that estimates the likelihood that the crania fit into the categories being tested. The posterior probabilities for all of the categories total to 1.000. The highest score is the most likely category for the skull. The typicality probability is also offered by F ordisc 2.0 but was not used in this analysis because it predicts the likelihood that the skull actually belongs to any of the categories being tested. Since all of the skulls are known to be Native American, the typicality score is unnecessary. The Giles and Elliot discriminant functions were carried out using a calculator. In order to evaluate the race of a skull using the Giles and Elliot technique, the sex of the crania must be known. Giles and Elliot (1963) provide formulae for determining sex. Once the sex has been determined, the appropriate race formula can be chosen. The formulae can be found in Table 2. 16 Table 2. Giles and Elliot Formulae 1.16(maximum cranial length) + 1.66(cranial base Sex length) + 3.98(bizygomatic diameter) - l.00(basion- rosthion) + 1.54(upper facial height) A score below 891.12 is female. 0.10(basion-prosthion) - 0.25(maximum cranial length) - 1.56(maximum cranial breadth) + 0.73(basion-bregma) - 0.29(cranial base length) + l.75(bizygomatic diameter) - 0.l6(upper facial height) - 0.84(nasal breadth) A score below 22.28 is White. Male: White vs. Native American 3.05(basion—prosthion) - 1.04(maximurn cranial length) - 5.41(maximum cranial breadth) +4.29(basion—bregma) - 4.02(cranial base length) + 5.62(bizygomatic diameter) - l.00(upper facial height) - 2.19(nasal breadth) A score below 130.1 is White. Female: White vs. Native American Therefore, if a skull was determined to be male the following would be the procedure for determining the race. The basion-prosthion length would be multiplied by 0.10. From this would be taken 0.25 multiplied by the maximum cranial length. From this would be subtracted 1.56 times the maximum cranial breadth. To this would be added 0.73 times the basion-bregma length. From this would be subtracted 0.29 times the cranial base length. To this would be added 1.75 times the bizygomatic diameter. From this would be taken 0.16 times the upper facial height. Finally, 0.84 times the nasal breadth would be subtracted from this. If the total score fell below 22.28, the skull should be considered White. The Gill et a1. (1988) method consists of three indices briefly outlined previously. The first index, maxillofrontal, is determined by dividing the naso-maxillofrontal subtense by the maxillofrontal breadth and multiplying by 100. If the result is less than 40, the skull is Native American. The zygoorbital index is the naso-zygoorbital subtense divided by the zygoorbital breadth and multiplied by 100. A score of less than 38 is Native American. Finally, the alpha index is the naso-alpha subtense divided by the 17 alpha cord and multiplied by 100. Results less than 60 are considered to be Native American. The ancestry would be the average of the three indices. Thus, if a skull received two Native American scores and a White score, it would be considered Native American. Fordisc 2.0 classifies a skul based on the input of certain measurements into discriminant functions. The person using the program inputs the measurements and selects the races he/she thinks the skull may be. The program then produces two scores for each racial category chosen. The scores are the posterior probability and the typicality probability. For this research, the hightest posterior probability score indicates the rae. For example, if a skull scored .750 for American Indian male and .250 for American Indian female, I would record the skull as an American Indian male. Each method is evaluated by its success rate. Since all of the crania are Native American, a positive Native American identification is considered a success for that method. The total number of correct racial determinations is divided by the number of skulls analyzed by each method for a percentage representation of success. An ideal method of analysis would have a success rate of 100%, meaning that it would correctly assign Native American race to all 81 skulls. 18 Results All of the measurements recorded for this study can be found in Appendix A. The worksheets for each method can be found in Appendices B and C. The numerical data were entered into a spreadsheet program where they were then manipulated according to the requirements of each technique to produce a racial affiliation. Of the 81 crania, 43 were judged by the author to be male and 38 were female. Figure 1. Actual Sexes of the Sample Native American Females Native American 47% Males 53% 19 Due to the flexible, accommodating nature of Fordisc 2.0, even those samples lacking several measurements could be analyzed. Therefore, all 81 sammes were run through Fordisc 2.0. Seventy-three were considered Native American (26 males and 47 females) and 8 were White (1 male and 7 females). The posterior probabilities ranged from 1.000 (a perfect score) to .445, with an average score of .845. Most scores were between .800 and 1.000, a strong indication of category. Figure 2. Fordisc 2.0 Results White Females 9% Pl White Males _.\ Native American 1 % Males 32% Native American Females 58% Of the 81 specimens, 45 were complete enough to be analyzed for sex by the Giles and Elliot (1963) formula. The rest of the skulls were too fragmentary for analysis. 20 Fifteen were determined to be female and 33 were male. Of those, 44 were able to be analyzed by the Giles and Elliot formulae for race. Sixteen males were determined to be Native American, 14 males were White, 10 females were Native American, and 4 females were White. ‘ Figure 3. Giles and Elliot Results White Females 9% Native American Males 36% White Males 32% Native American Females 23% Fifty-eight of the original 81 skulls were usable for the Gill et al. (1988) indices. Thirty-nine were considered Native American and 19 were considered White. Giles and Elliot properly assigned the sex of 15 of 24 females (63%) and 24 of 24 (100%) males. This is an overall success rate of 81%. Fordisc 2.0 properly assigned 35 of 38 (92%) females and 23 of 43 (53%) males for a success rate of 72%. 21 The results of each test were recorded as positive or negative with a 1 indicating a race of Native American and 0 White. If insufficient data was available, the field was left blank. The results of this system can be found in Appendix D. ' The mean score for Fordisc 2.0 was 0.90 or 90% (73 of 81), Giles and Elliot was 0.68 or 68% (30 of 44), and Gill was 0.67 or 67% (39 of 58). These are the percentages of correct racial affiliation. A z-score test for difference in proportions was conducted to determine the significance of the difference between the proportion of crania classified as Native American in the sample, using each different method and the actual proportion of Native Americans in the sample (1.0). The null hypotheses I am testing is that the proportion correctly classified using each method is equal to the actual proportion of Native Americans. The alternative hypothesis is that the proportion correctly classified using each method is not equal to the actual proportion of Native Americans in the sample. The calculated z-score using F ordisc 2.0 to determine race is —2.96; the z-score using Giles and Elliot for classification is —6.08; and the z-score using Gill for classification is —6.23. This means that the proportion correctly classified using each of these methods differed from the known proportion of Native Americans in the sample. Using the common alpha level of .05, a calculated z-score between —1.96 and 1.96 indicates that the method being tested properly predicted the race of the skull samples. All three methods tested resulted in z-scores that were outside the +/- 1.96 range, indicating that none of the methods were acceptable for determining race in a sample of Native Americans remains found in Michigan. F ordisc 2.0 resulted in the lowest z-score (2.96), but this score still . indicates that this method is not reliable when using remains from the Michigan sample. 22 Of interest with the Gill et a1. method was the actual range of scores for the samples. Though the overall results of the testing showed that the method was not very effective at separating White from Native American remains in the Michigan sample (67% accurate), it is interesting to note that the scores were generally close to the cut off marks assigned by the authors. Many were within 5 points of the sectioning points leaving the possibility that the Michigan sample is simply less distinctive based on this technique and an adjustment of the sectioning points would prove more successful. This further suggests that the Michigan population has some kind of genetic or environmental difference from the other populations tested. This could be coincidental and a product of sampling error but it could also be a sign of a larger trend. A similar statement could not be made of the Giles and Elliot data. Those scores generally fell far above or below the sectioning point of 22.28, ranging from a low of -1 2 to a high of 48 for males. For females, the sectioning point was 130.1 with a range of 47 to 190. 23 Discussion Though not the focus of this work, it is important to note how well Giles and Elliot and F ordisc 2.0 sexed the samples since the racial assignment in both cases was based on the determined sex. The skulls were sexed as they were examined by the author based on morphological features characteristic of males and females as described in Bass (1995) and White (2000). There were no postcranial remains associated with the crania. The sex assigned by this visual method was compared to that assigned by each method. Giles and Elliot properly assigned 15 of 24 females (63%) and 24 of 24 (100%) males. This is an overall success rate of 81%. F ordisc 2.0 properly assigned 35 of 38 (92%) females and 23 of 43 (53%) males for a success rate of 72%. Of note here is that F ordisc 2.0 was just as likely to correctly classify a male skull as male as it was to incorrectly classify it as female. However, this tendency did not seem to have a great effect on the racial determination since Fordisc 2.0 outperformed the Giles and Elliot method in the end. The basic data relating to racial assignment is relatively easily deciphered. The first and most important point is that the means are contrasting. F ordisc 2.0 classifies 90% of the skulls correctly while the other two methods only correctly assign 67% (Gill et al.) and 68% (Giles and Elliot). The reason for this difference is most likely the samples used to create the individual methods. Fordisc 2.0 was created using a variety of skeletal samples from across the continent, though admittedly focused on Southwestern and Great Plains tribes, whereas the others were created using more restricted sampling. Giles and Elliot (1962) used only one collection from one archaeological site (Indian Knoll). The Gill et al. (1988) method was developed using an assortment of Great Plains 24 samples and was tested (Gill and Gilbert, 1990) on samples from other regions. However, it was not tested in the northern Midwest particularly. 25 Conclusion Eighty-one crania from the Upper and Lower Peninsulas of Michigan were measured. Based on contextual and scientific evidence, they were all considered prehistoric and therefore Native American. Three methods for determining race were tested. The first was authored by Giles and Elliot in 1962. It is a series of discriminant functions into which measurements are input to receive a sex and race for the remains. This method was created using the Hamman-Todd collection, Terry collection, and a Native American sample from the Indian Knoll stie in Kentucky. The second method was authored by Gill et al. in 1988. This is a series of indices based on measurements of the skull. It was created using the Terry collection and a Native American sample of various Great Plains tribes. The last, Fordisc 2.0, is a computer program developed by Ousley and Jantz in 1996. his also a series of discriminant functions that discriminates between certain races. It was developed using forensic cases compiled in the Forensic Databank and various Native American samples from the southwest and Great Plains. Of the three, Fordisc 2.0 performed the best with a success rate of 90%. A higher success rate means that the method is more reliable. The Giles and Elliot method and Gill et al. method were unable to properly determine the race of the samples (68% and 67% accuracy respectively) and should not therefore be used in the Great Lakes region. This is most likely caused by the lack of extensive testing of these methods around the Great Lakes region. Their sample populations were simply too restricted and not widely enough tested to allow for a great amount of genetic and environmentally created variation in the human skull. Fordisc 2.0, probably because it was created using a 26 continually expanding database of site and forensic data, is best equipped to make an educated prediction about the race of the Michigan sample of crania. I had originally hypothesized that Fordisc 2.0 would work well for my Michigan population, meaning it would have little significant error. I further hypothesized that the Gill et al. and Giles and Elliot methods would not perform well. My expectations were proven wrong by my research. Though F ordisc 2.0 worked the best of the three methods, it still produced significant error. Therefore, all three techniques for determining race produced significant error. This study suggests that the native peoples of Michigan were physically similar but significantly different from their counterparts in other parts of the country, particularly those of the central mid-west and the northern plains. If true, this could mean that more work is necessary to determine exactly what kind of relationship native peoples of Michigan had with people elsewhere on the continent and the consequences of these relationships on the genetic makeup of the population. It is possible that the Michigan populations developed more localized physical features as a result of genetic drift and social and physical barriers. These should be explored more fully by further studies of physical and genetic variation among and between populations of the Michigan area and other areas. It is also possible that the Michigan sample is merely representative of a point in a continuum of trait manifestations. The other areas of the United States could simply be on a different part of the continuum. This continuum, however, has not yet been documented. These kinds of studies would allow forensic anthropologists to make better predictions about race when presented with Native American skulls. Further tests should be conducted nationally to validate these and other methods for determining race 27 of Native American remains before use and reliance, as this study has shown that there could be significant variation among populations. Such methods, including those tested here, could be improved by the inclusion of a greater variety of broader data sets. Forensic anthropologists need to be sure that the methods they are using are accurate and reliable in their area. 28 APPENDICES 29 Crania Number cranial length cranial breadth bizygomatic basion-bregma cranial base It. basion-prosth. maxillo-alv. Br. maxillo-alv. Lt biauricular upper facial ht min. frontal br. upper facial br. nasal ht. nasal br. ortibal br. orbital ht. biorbital br. interorbital br. frontal chord parietal chord occipital chord for. Mag. Lt. for. Mag. Br. mastoid It. I 182 133 131 99 108 62 126 51 102 117 43 31 112 31 109 115 98 40 31 32 Appendix A: Measurements Taken _2_ 182 130 103 128 101 97 51 52 119 63 98 103 49 27 38 32 99 27 109 103 99 37 32 26 3 174 141 148 137 104 90 65 131 54 95 109 45 34 39 32 105 26 110 108 101 36 29 29 3 182 129 133 107 104 68 52 124 77 90 103 56 32 38 35 25 103 103 100 35 31 26 5 191 130 146 141 109 97 68 53 134 72 99 115 52 32 42 34 110 32 114 110 96 38 34 36 30 6 181 135 117 130 101 98 65 54 123 62 91 103 46 27 41 35 99 22 106 110 101 34 30 29 _Z 180 134 128 134 99 96 65 52 125 63 92 103 44 25 38 32 98 22 1 12 109 97 41 31 26 _8 185 140 141 138 109 110 65 60 130 71 95 108 50 31 42 33 101 22 111 116 96 39 35 31 _9_ 185 143 144 109 106 55 133 71 102 53 32 45 38 28 121 109 100 36 30 34 _1_Q 176 i 140 127 127 104 102 57 130 71 94 107 49 26 44 33 101 22 109 105 94 35 31 31 _l_l 165 130 129 96 94 58 50 116 58 85 95 47 23 35 32 87 19 110 101 90 34 29 18 Crania Number cranial length cranial breadth bizygomatic basion-bregma cranial base 1t. basion-prosth. maxillo-alv. Br. maxillo-alv. Lt biauricular upper facial ht min. frontal br. ' upper facial br. nasal ht. nasal br. ortibal br. orbital ht. biorbital br. interorbital br. frontal chord parietal chord occipital chord for. Mag. Lt. for. Mag. Br. mastoid 1t. 12 187 140 139 135 109 105 70 58 137 75 96 111 54 28 42 35 105 23 113 110 93 35 29 33 _1_3 179 146 151 135 106 102 65 53 137 75 99 112 50 28 41 37 102 17 121 104 91 40 35 31 1_4 183 159 134 106 106 68 64 147 73 99 1 12 50 29 42 35 118 112 98 41 36 35 1_5_ 187 160 144 111 97 66 49 144 69 107 112 52 30 45 35 101 20 123 114 102 35 34 24 _1_6 166 121 134 101 94 91 45 105 67 87 92 52 22 '38 35 86 19 104 103 91 38 28 19 31 _1_'_7_ 167 140 126 128 98 92 68 50 128 63 94 107 51 34 41 34 99 26 109 101 92 37 32 28 1_8_ 166 140 130 127 97 99 60 53 121 68 90 102 50 25 43 32 92 22 105 103 96 30 26 26 122921 176 134 125 104 101 62 53 124 67 94 103 54 27 37 34 95 20 105 102 93 33 31 39 178 135 132 131 103 66 128 89 99 52 26 39 32 99 21 110 107 99 35 30 26 179 137 130 129 94 93 64 52 126 65 95 104 51 27 41 35 98 20 1 16 107 94 34 29 26 _2_; 186 148 140 112 110 66 60 139 78 103 116 51 24 43 36 106 22 130 104 104 36 31 34 Crania Number cranial length cranial breadth bizygomatic basion-bregma cranial base It. basion-prosth. maxillo-alv. Br. maxillo-alv. Lt biauricular upper facial ht min. frontal br. upper facial br. nasal ht. nasal br. ortibal br. orbital ht. biorbital br. interorbital br. frontal chord parietal chord occipital chord for. Mag. Lt. for. Mag. Br. mastoid 1t. 23 160 120 125 90 91 62 56 117 63 85 93 98 25 36 30 87 18 102 101 87 29 24 23 E 170 151 137 105 100 64 52 135 69 96 105 55 27 40 33 96 26 116 101 93 35 32 25 2_5__ 171 135 133 121 96 99 59 53 123 66 99 108 47 25 42 35 100 22 111 102 90 35 28 22 2.621% 178 135 134 100 97 59 49 124 64 48 29 42 34 21 107 117 98 35 28 26 193 147 125 135 102 98 61 53 123 75 101 106 57 24 42 36 97 16 119 113 109 42 34 35 32 180 135 129 135 104 95 '61 51 124 64 96 106 52 30 45 37 100 19 112 115 95 37 33 28 19. 177 138 140 133 96 94 67 54 128 66 96 109 49 27 42 36 104 24 110 106 95 36 26 30 _3_Q 166 130 123 93 100 60 55 118 65 90 100 45 25 39 32 * 95 21 108 100 94 34 28 22 Q 187 146 133 137 95 91 59 54 123 73 106 110 54 25 41 37 100 21 114 120 108 36 31 33 32 174 133 135 100 98 66 53 119 68 92 96 49 27 34 36 87 23 109 108 29 3_3_ 185 136 131 130 103 105 61 58 120 65 91 103 46 24 40 33 94 19 111 116 91 38 30 29 cmaNumber2435363z'383249 H42 43% cranial length cranial breadth bizygomatic basion-bregma cranial, base It. basion-prosth. maxillo-alv. Br. maxillo-alv. Lt biauricular upper facial ht min. frontal br. upper facial br. nasal ht. nasal br. ortibal br. orbital ht. biorbital br. interorbital br. frontal chord parietal chord occipital chord for. Mag. Lt. for. Mag. Br. mastoid It. 188 136 142 106 95 70 54 126 82 95 1 10 55 31 43 35 27 112 114 108 35 30 28 170 144 136 135 95 94 51 49 126 71 97 103 50 30 41 37 96 22 110 103 101 30 28 25 182 134 137 127 103 93 54 49 129 63 87 107 52 26 44 35 102 23 109 104 100 38 29 29 175 125 132 101 93 61 47 122 64 87 104 49 26 37 32 93 19 107 103 105 37 30 31 184 139 125 123 98 90 59 53 118 66 99 107 52 25 42 35 102 25 100 114 106 37 32 25 33 154 137 119 120 88 87 61 45 120 59 86 98 45 28 35 34 89 20 105 101 83 31 27 21 185 133 137 108 52 127 97 106 53 26 44 34 97 17 118 113 102 35 31 30 193 141 121 135 103 90 46 47 119. 70 98 99 53 23 42 38 ~92 17 114 119 101 39 30 25 176 134 129 130 100 97 58 53 122 67 91 103 51 23 42 35 93 19 112 106 97 36 31 22 172 135 138 133 95 94 54 122 78 90 106 58 27 43 37 98 21 1 13 105 93 36 27 27 170 135 124 98 96 65 47 129 60 91 99 45 25 39 35 95 17 107 107 86 37 34 25 CmniaNumberfléiéélfléQéQélflfiéflfi cranial length cranial breadth bizygomatic basion-bregma cranial base It. basion-prosth. maxillo-alv. Br. maxillo-alv. Lt biauricular upper facial ht min. frontal br. upper facial br. nasal ht. nasal br. ortibal br. orbital ht. biorbital br. interorbital br. frontal chord parietal chord occipital chord for. Mag. Lt. for. Mag. Br. mastoid It. 135 145 103 110 69 63 125 71 96 109 52 27 43 33 98 22 117 33 31 30 178 128 128 135 104 98 48 49 120 61 88 53 23 40 33 95 20 110 112 96 33 27 26 183 140 133 128 104 104 64 57 127 72 98 113 52 27 44 35 100 20 111 108 95 33 29 31 174 134 131 102 103 63 57 123 74 93 105 .52 25 42 34 94 21 110 106 88 37 28 27 184 145 147 133 104 96 63 55 136 70 92 108 54 28 42 33 99 19 116 107 98 33 26 25 34 170 136 131 96 89 61 44 121 66 87 102 50 28 42 33 92 17 109 109 91 35 28 24 177 139 130 130 102 103 67 57 125 67 91 101 52 27 40. 33 91 20 112 106 92 37 30 27 179 140 140 133 101 98 68 54 131 50 96 109 50 27 43 32 '95 17 109 108 99 34 30 29 170 133 129 96 97 64 51 116 67 87 94 52 28 38 33 88 19 105 105 92 39 27 29 179 132 133 102 97 66 55 121 71 93 108 53 29 43 35 102 21 114 107 99 41 31 26 172 130 126 94 88 57 49 6o 90 97 47 28 40 35 89 20 107 102 99 35 28 Crania Number cranial length cranial breadth bizygomatic basion-bregma cranial base It. basion-prosth. maxillo-alv. Br. maxillo-alv. Lt biauricular upper facial ht min. frontal br. upper facial br. nasal ht. nasal br. ortibal br. orbital ht. biorbital br. interorbital br. frontal chord parietal chord occipital chord for. Mag. Lt. for. Mag. Br. mastoid 1t. 56 175 139 144 134 105 105 69 60 129 74 95 113 56 32 47 36 102 22 108 114 102 35 31 31 _5_Z 172 128 124 99 94 64 52 123 61 90 98 49 26 40 34 91 16 107 109 92 38 3 1 21 _5__8 176 129 130 103 99 68 52 125 72 86 105 54 31 43 34 98 23 106 1 10 90 41 33 30 52 173 140 132 108 104 106 52 133 63 94 109 51 29 43 32 100 19 106 100 106 33 27 26 ®6_1 188 146 154 137 112 113 74 62 141 78 97 116 55 30 45 34 111 23 116 106 102 39 33 32 35 174 134 135 102 100 56 56 126 70 92 104 53 25 43 36 101 23 113 97 98 35 30 28 Q: 191 135 136 138 109 96 64 50 128 75 97 106 56 26 41 37 94 22 119 110 104 36 27 31 _6_; 166 134 127 122 96 99 58 54 122 65 101 47 23 36 36 ,88 23 99 106 85 37 31 26 63.6.5. 173 150 152 132 100 104 74’ 61 142 80 94 113 57 28 45 36 98 20 114 99 101 35 40 27 186 140 145 141 108 108 67 56 134 75 97 112 56 27 43 38 97 18 113 104 23 6_6_ 179 132 131 129 100 100 63 52 121 64 89 101 46 25 40 32 91 19 102 118 92 37 30 27 Crania Number 61 68 cranial length cranial breadth bizygomatic basion-bregma cranial base It. basion-prosth. maxillo-alv. Br. maxillo-alv. Lt biauricular upper facial ht min. frontal br. upper facial br. nasal ht. nasal br. ortibal br. orbital ht. biorbital br. interorbital br. frontal chord parietal chord occipital chord for. Mag. Lt. for. Mag. Br. mastoid It. 187 138 140 110 107 67 58 127 78. 93 111 56 27 41 37 101 24 117 111 98 36 30 32 172 145 130 132 103 102 .65 54 127 73 92 103 55 26 45 35 92 18 107 11 1 93 37 29 28 59.2911. 175 138 127 90 90 62 46 121 .64 92 98 47 26 37 32 . 90 18 111 112 92 34 27 21 180 135 120 100 100 62 57 123 76 86 103 55 28 42 39 95 20 102 38 33 24 179 134 132 103 107 66 58 123 68 90 107 49 27 44 33 98 21 106 100 104 37 28 23 36 12 167 130 128 131 101 100 62 57 124 70 88 97 49 27 37 37 91 21 107 107 88 32 27 27 _‘Z_3_ 170 134 133 123 95 91 61 46 126 57 96 104 46 25 42 32 14 184 135 131 102 98 66 54 126 81 89 54 24 38 36 96~ 20 1 12 104 95 30 29 20 19 111 117 92 37 31 29 7_5 176 140 136 137 100 97 62 53 126 71 95 103 51 29 41 37 96 21 113 101 106 39 30 25 7_6 174 135 134 135 105 92 122 67 93 104 54 28 40 33 96 24 l 1 l 35 30 28 11 184 132 136 104 96 63 48 l 17 69 97 103 49 24 42 37 92 23 1 14 122 29 25 Crania Number cranial length cranial breadth bizygomatic basion-bregma cranial base It. basion-prosth. maxillo-alv. Br. maxillo-alv. Lt biauricular upper facial ht min. frontal br. upper facial br. nasal ht. nasal br. ortibal br. orbital ht. biorbital br. interorbital br. frontal chord parietal chord occipital chord for. Mag. Lt. for. Mag. Br. mastoid 1t. 18 167 126 98 97 62 54 1 15 68 85 51 23 39 36 20 104 99 92 33 31 22 186 145 138 109 101 71 52 140 69 98 114 55 29 43 35 103 25 117 115 91 37 3O 37 _8_Q 180 133 139 127 104 103 60 56 127 67 89 107 45 28 40 35 96 21 106 108 103 38 28 24 81 176 131 127 131 100 100 66 56 121 69 90 102 50 24 49 33 96 22 108 105 91 35 31 23 Sex by G&E Number ooqoxmpwwv— wwWWUJWNNNNNNNNNND—‘F—iI—‘I—‘F—‘r—it—‘F—ib—‘I—fi kh-hbJNF-‘OOWQQMAWNflowmfiakl’IkIJJNF—‘oc L») 0‘1 Appendix B: Giles and Elliot Data MCL 182 182 174 182 191 181 180 185 185 176 165 187 179 183 187 166 167 166 176 178 179 186 160 170 171 178 193 180 177 166 187 174 185 188 170 182 CBL 99 101 104 107 109 101 99 107 109 104 96 109 106 106 - 1 1 1 101 98 97 104 103 94 l 12 90 105 96 100 102 104 96 93 95 100 103 106 95 103 Bizyg 130 148 146 117 128 141 128 139 151 126 130 134 132 130 148 120 133 125 129 140 133 131 136 137 Ba—Pr 108 97 90 104 97 98 96 1 10 106 102 94 105 102 106 97 94 92 99 101 93 1 10 91 100 99 97 98 95 94 100 91 98 105 95 94 UFH 93 38 51 63 54 77 72 62 63 71 71 71 58 75 75 73 69 67 63 68 67 99 65 78 63 69 66 64 75 64 66 65 72 68 65 82 71 ‘63 product 346 896.2 956.68 403.32 997.46 840.76 883.6 952.74 398.88 893.58 346.08 961.58 998.08 394.66 410.44 369.4 862.9 876.7 912.3 1055.3 888.18 1000.8 818.62 377.76 889.7 374.04 908.2 898.42 929.52 347.04 923.84 374.56 902.06 425.32 91 1.52 931.38 891 . 12? n/a male male n/a male female female male n/a male n/a male male n/a n/a n/a female female male n/a female male female n/a female n/a male male male n/a male n/a male n/a male male 37 38 39 40 41 42 43 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 175 184 154 185 193 176 172 170 178 183 174 184 170 177 179 170 179 172 175 172 176 173 188 174 191 166 173 186 179 187 172 175 180 179 167 170 184 176 174 184 101 98 88 108 103 100 95 98 103 104 104 102 104 96 102 101 96 102 94 105 99 103 108 112 102 109 96 100 108 100 110 103 90 100 103 101 95 102 100 105 104 125 119 121 129 138 128 133 147 130 140 144 154 136 127 152 145 131 130 135 128 133 136 134 39 93 90 87 90 97 94 96 1 10 98 104 103 96 89 103 98 97 97 88 105 94 99 104 1 13 100 96 99 104 108 100 107 102 90 100 107 100 91 98 97 92 96 64 66 59 70 67 78 60 71 61 72 74 70 66 67 50 67 - 108 60 74 61 72 63 78 70 74 65 80 75 64 1 11 73 64 76 68 70 57 81 71 67 69 376.22 885.26 802.2 393.88 894.24 889.76 932.58 356.28 170.32 884.5 921.14 382.12 982.94 369.2 892.22 ' 911.5 362.74 446.28 359.96 959.38 363.8 387.02 372.98 1024 378.96 961.74 858.48 990.84 979.64 893.58 463.46 898.32 360.96 929.14 376.34 878.62 881.02 409.5 923.78 920.64 396.34 n/a female female n/a male female male n/a n/a female male n/a male n/a male _ male n/a n/a n/a male n/a n/a n/a male n/a male female male male male n/a male n/a male n/a female female n/a male male n/a 78 79 80 81 Male Race by G & E Number OONQM-fiWN—t WMNNNNNNNNNNI—t—Au—A—Iu—u—AHHI—tpd floomNO‘M-hiA’NI—‘OCOONQMAUJNHO0 167 186 180 176 Ba-Pr 97 90 97 110 102 105 102 101 110 98 95 94 91 MCL 182 174 191 185 176 187 179 176 186 193 180 177 187 98 109 104 100 139 127 MCB 130 141 130 140 140 140 146 147 135 138 146 Ba-B 128 137 141 138 127 135 135 125 140 135' 135 133 137 CBL 101 104 109 107 104 109 106 104 112 l 02 1 04 96 95 40 97 101 103 100 Bizyg 130 148 146 141 128 139 151 134 148 125 129 140 133 UFH 63 54 72 71 71 75 75 67 78 75 64 66 73 68 98 67 69 NB 364.12 446.62 934.84 881.88 product 27 20 34 37 32 48 31 25 26 1.2 28 20 28 34 27 24 24 -12 30 13 27 31 25 7.1 n/a n/a male female 22.28? white AL AL A.I. white white A.I. n/a white white A.I. white 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 105 94 93 90 94 104 96 103 98 105 113 96 104 108 100 102 100 185 170 182 193 172 183 184 177 179 175 188 191 173 186 179 172 180 136 144 .134 141 135 140 145 139 140 139 146 135 150 140 132 145 130 135 127 135 133 128 133 130 133 134 137 138 132 141 129 132 120 103 95 103 103 95 104 104 102 101 105 112 109 100 108 100 103 100 41 131 136 137 121 138 133 147 130 140 144 154 136 152 145 131 130 135 65 71 63 70 78 72 108 67 50 74 78 75 1 13 75 64 73 76 24 30 26 23 27 27 28 27 27 32 30 26 28 '27 25 26 28 16 15 25 32 8.1 21 8.6 29 31 36 25 25 37 23 1.5 white white A.I. white A.I. white white white A.I. A.I. A.I. A.I. A.I. A.I. A.I. white n/a 73 74 75 76 77 78 79 80 81 Female Race by G&E Number mQONM-bWNu—s QMAUJNHOCWNONM-hWN—‘Oo 97 92 103 Ba-P 98 96 92 99 93 91 99 176 174 180 MCL 181 180 167 166 179 160 171 140 135 133 MCB 135 134 140 140 137 135 137 135 127 Ba-B 130 134 128 127 129 125 121 100 105 104 CBL 101 99 98 97 94 90 96 136 134 139 42 Bizyg 117 128 126 130 130 120 133 71 67 67 UF H 62 63 63 68 65 63 66 NB 28 29 28 21 23 29 27 25 34 25 27 25 25 product 68 159 75 135 138 154 white A.I. A.I. 130.1? white A.I. white A.I. A.I. A.I. 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 6O 61 62 63 64 65 66 67 90 87 97 98 99 184 154 176 178 166 139 137 134 128 134 123 120 130 135 122 98 88 100 104 96 43 125 119 129 128 127 66 59 67 61 65 25 28 23 23 23 47 74 151 190 140 white white A.I. A.I. A.I. 68 69 70 71 72 73 74 75 76 77 78 79 80 81 100 91 100 167 170 176 130 134 131 131 123 131 101 95 100 44 128 133 127 70 57 69 27 24 24 174 159 165 A.I. A.I. A.I. Z muomrswwi—o WWWUJUJNNNNNNNNNNt—fit—tu—su—tu—Au—tu—‘r—tp—tu—A DWNfloomflONM#WNl—‘OOOONON'Jl-PUJN—‘OC b.) VJ! Max . Sub. 10 10 12 10 11 10 11 13 \OOO-h i—AI—au—tu—h WNNI—‘QQOO AMMQOONOOOONQQO‘O fl MFB 31 27 26 25 32 18 20 21 28 19 14 20 16 20 17 26 22 20 19 19 21 13 19 19 20 16 18 24 21 18 19 17 17 22 NZ Sub 19 17 20 21 22 20 20 23 22 17 21 18 19 20 17 16 20 20 23 21 16 23 22 17 20 ZOB 53 65 53 62 53 52 50 _ 54 53 54 58 47 68 51 50 47 54 56 53 48 60 49 48 50 58 51 48 48 52 55 53 Appendix C: Gill et al. Data NA Sub 12 14 14 16 16 13 17 16 11 14 13 11 13 13 15 11 19 13 18 15 12 10 Alpha 34 33 37 28 27 32 35 27 25 25 28 33 33 18 20 14 23 22 22 25 29 22 22 23 24 23 27 24 23 27 Max Index 32.26 37.04 46.15 40.00 34.38 33.33 50.00 52.38 46.43 42.11 28.57 40.00 56.25 40.00 41.18 61.54 50.00 60.00 63.16 42.11 42.86 46.15 36.84 36.84 30.00 56.25 50.00 37.50 33.33 44.44, 36.84 29.41 29.41 63.64 45 40 A.I. A.I. W either A.I. A.I. W W W W A.I. either W either aaiiiaaaaaaaa g???g?? Zygo Index 35.85 0.00 0.00 32.08 32.26 39.62 42.31 40.00 37.04 43.40 0.00 37.93 36.17 0.00 0.00 42.00 38.30 35.19 35.71 32.08 33.33 33.33 40.82 47.92 42.00 0.00 31.37 47.92 45.83 32.69 36.36 0.00 38 A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. Alpha Index 0.00 0.00 0.00 42.86 51.85 43.75 0.00 59.26 52.00 0.00 48.48 61.1 1 70.00 64.29 56.52 50.00 59.09 52.00 51.72 50.00 86.36 56.52 0.00 78.26 55.56 50.00 43.48 0.00 6O A.I. A.I. A.I. 64.00 W 60.71 W A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. total 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 O\O\OOC\\lr-‘OO\I\IOUI\I\IO\\O\I\OQM\I\OOO\O\)\IOOUIOO\IO\O\UIO\\I\IOO\OH\O©\I 16 19 17 20 14 15 18 19 17 18 17 18 20 17 16 18 19 18 21 20 18 16 18 20 20 18 19 19 15 16 15 19 14 17 19 21 .18 15 19 '16 19 24 21 21 22 22 21 21 17 19 19 23 23 16 22 17 16 21 21 18 19 21 21 21 10 20 19 19 21 26 18 22 17 18 21 19 57 55 52 49 49 '54 54 50 62 56 62 60 57 52 54 55 53 56 60 57 54 57 56 66 54 55 59 58 52 55 55 51 59 65 .48 53 59 55 17 14 13 17 13 11 10 11 16 11 13 ll 11 12 14 13 12 16 12 13 15 19 10 13 13 13 11 14 26 23 27 20 22 22 19 25 19 21 20 18 22 15 16 18 19 26 24 21 25 20 24 26 24 22 25 25 23 24 24 24 21 26 25 24 27 43.75 47.37 52.94 55.00 64.29 53.33 38.89 36.84 35.29 27.78 35.29 33.33 35.00 47.06 31.25 44.44 36.84 38.89 42.86 40.00 50.00 43.75 27.78 30.00 45.00 38.89 47.37 31.58 46.67 43.75 33.33 52.63 50.00 41.18 42.1 1 52.38 38.89 40.00 42.11 37.50 31.58 46 aaaaaiaaia A.I. either W A.I. A.I. 42.11 38.18 0.00 42.86 44.90 40.74 38.89 42.00 27.42 33.93 30.65 38.33 40.35 30.77 40.74 30.91 30.19 0.00 35.00 36.84 33.33 33.33 37.50 31.82 38.89 18.18 33.90 32.76 36.54 38.18 47.27 35.29 37.29 0.00 35.42 33.96 35.59 34.55 A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. iiaiaaiitaaaaa a a 65.38 60.87 0.00 65.00 77.27 59.09 57.89 36.00 52.63 52.38 45.00 50.00 72.73 46.67 68.75 50.00 47.37 0.00 54.17 52.38 44.00 40.00 50.00 53.85 54.17 54.55 64.00 48.00 56.52 62.50 79.17 41.67 61.90 50.00 52.00 45.83 51.85 A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. A.I. 77 78 79 80 81 00 O\ 23 20 25 18 17 20 17 58 60 61 14 15 ll 26 21 25 29 25 34.78 40.00 60.00 33.33 35.29 47 A.I. A.I. A.I. A.I. 0.00 33.33 27.87 A.I. A.I. 0.00 0.00 56.00 51.72 44.00 A.I. A.I. A.I. n/a n/a n/a A.I. A.I. Sample # Appendix D: Results Fordisc 2.0 race 0 white 1 NA. Giles & Elliot race 0 white 1 NA. Gill race 0 white 1 NA. I—o O\OOO\IO\UI-th\J-‘ MWWWUJNNNNNNNNNN—‘flu—HHHr—A—t AWN—‘OOOONQUI-wa—‘OOOONQUIAWN— I—‘hzli—lI—l ~ A 00 t—‘h—a—Io 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 49 76 77 78 79 80 81 p—a—sHh‘o—a Mean 0.90 0.68 0.67 stand. dev. 0.30 0.47 0.47 50 Literature Cited Ayers HG., RL Jantz, PH Moore-Janson. 1990. Giles and Elliot Race Discriminant Fucntion Revisited: A test using recent forensic cases. In: Gill G and Rhine S, editors. Skeletal Attribution of Race; methods for forensic anthropology. Albuquerque: Maxwell Museum of Anthropology, Anthropological Papers No. 4 Bass WM. 1995. Human Osteology; A laboratory and field manual, fourth edition. Columbia, Mo.: Special Publication No. 2 of the Missouri Archaeological Society Brace CL. 1995. Region does not mean “race”- reality versus convention in forensic anthropology. In: Journal of Forensic Sciences 40(2): 171-175 - 1996. A four letter word called “race”. In: Race and Other Misadventures, Reynolds and Lieberman (eds), General Hall Publishers Buikstra, J E and DH Ubelaker, Eds. 1994. Standards for Data Collection fi'om Human Skeletal Remains. Research Series. F ayetteville, Arkansas Archaeological Survey. Cleveland Museum of Natural History. 2002 Collections and Research at the Museum. Cleveland, OH accessed Sept. 25, 2004 http://www.cmnh.org/collections/index.html Corcos A. 1997. The Myth of Human Races. East Lansing: Michigan State University Press Dolhinow P and V Sarich. 1971. Background for Man; Readings in Physical Anthropology. Boston: Little Brown & Co. Downs JF and HK Bleibtreu. 1969. Human variation; an introduction to physical anthropology. Beverly Hills: Glencoe Press Fisher TD. and GW Gill. 1990. Application of the Giles-Elliot discriminant function formulae to a cranial sample of northwestern Plains Indians. In: Gill G and Rhine S, editors. Skeletal Attribution of Race; methods for forensic anthropology. Pp 59-64 Albuquerque: Maxwell Museum of Anthropology, Anthropological Papers No. 4 Giles E and 0 Elliot. 1962. Race identification from cranial measurements. In Journal of Forensic Sciences 7: 147-1 57 - 1963. Sex determination by discriminant function analysis of crania. In: American Journal of Physical Anthropology 21 :53-68 Gill GW and BM Gilbert. 1990. Race identification from the midfacial skeleton: American Blacks and Whites. In: Gill G and Rhine S, editors. Skeletal Attribution 51 of Race; methods for forensic anthropology. Pp 4754 Albuquerque: Maxwell Museum of Anthropology, Anthropological Papers No. 4 Gill GW, SS Hughes, SM Bennett, and BM Gilbert. 1988. Racial identification from the midfacial skeleton with special reference to American Indians and Whites. In: Journal of Forensic Sciences 13 :92-99 Halloway RL. 2002. Head to head with Boas: did he err on the plasticity of head form? In: Proceedings of the National Academy of Sciences of the United States of America 99(23): 14622-3 Hinkes MJ. 1990. Shovel shaped incisors in human identification. In: Gill G and Rhine S, editors. Skeletal Attribution of Race; methods for forensic anthropology. Pp 21- 26 Albuquerque: Maxwell Museum of Anthropology, Anthropological Papers No. 4 Hunt DR 2004 The Robert J. Terry Anatomical Skeletal Collection. Department of Anthropology, National Museum of Natural History, Smithsonian Institution accessed Sept. 25, 2004 http://www.nmnh.si.edu/anthro/cm/terry.htm Iscan MY. 1990. A comparison of techniques on the determination of race, sex, and stature from the Terry and Hamann-Todd collections. In: Gill G and Rhine S, editors. Skeletal Attribution of Race; methods for forensic anthropology. Pp 73- 82 Albuquerque: Maxwell Museum of Anthropology, Anthropological Papers No. 4 Kennedy KAR. 1995. But professor, why teach race identification if races don’t exist? In: Journal of Forensic Sciences 40(5): 797-800 Lahr M. 1996. The Evolution of Modern Human Diversity; a study of cranial variation. New York: Cambridge University Press Lieberman LL, Stevenson BW, Reynolds LT. 1989. Race and anthropology: A core concept without consensus. In: Anthropology and Education Quarterly 20(2):67- 73 Livingstone FB. 1964. On the nonexistence of human races. In: The Concept of Race. M. F. A. Montagu (ed), Collier. Molnar S. 1975. Races, Types, and Ethnic Groups; the problem of human variation. Upper Saddle River, NJ: Prentice Hall Inc., - 2002. Human Variation; races, types and ethnic groups, fifth edition. Englewood Cliffs, NJ: Prentice Hall Inc. 52 Native American Graves and Repatriation Act. 25 U. S. C. 3001 et seq. 1990 as found at http: //www. cr. nps. gov/nagpra/ in May, 2005 Ousley SD and RL Jantz. 1996. Fordisc 2.0; Personal computer forensic discriminant functions, user’s guide. Knoxville, University of Tennessee Sarich V and F Miele. 2004. Race; the Reality of Human Differences. Cambridge: Westview Press Sauer NJ. 1992. Forensic anthropology and the concept of race: if races don’t exist, why are forensic anthropologists so good at identifying them?. In: Social Science and Medicine. 34(2): 107-11 1 Sparks CS and RL J antz. 2002. A reassessment of human cranial plasticity: Boas revisited. In: Proceedings of the National Academy of Sciences of the United States of America 99(23): 14636-9 Steward J. 1945. The changing American Indians. In: Linton R, editor. The Science of Man in the World Crisis. 282-305 Columbia: Columbian Press Ubelaker DH. 1998. Fordisc 2.0: personal computer forensic discriminant functions. In: International Journal of Osteoarchaeology 8:128-133 Ubelaker D, A Ross, and S Graver. 2002. Application of forensic discriminant functions to a Spanish cranial sample. In: Forensic Science Communications 4(3): 1-6 United States Census Bureau, 2000. http://factfinder.census.gov/home/saff/main.html. accessed September, 2004. Washbum SL. 1963. The study of race. In: American Anthropologist 65(3) part 1: 521- 531 White TD. 2000. Human Osteology, second edition. San Diego: Academic Press Williams FL, RL Belcher, and GL Armelagos. 2005. Forensic misclassification of ancient Nubian crania: implications for assumptions about human variation. In: Current Anthropolog) 46(2): 340-7 53 IJilinIn