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Record W2976374911 · doi:10.7202/1064504ar

Exploring Potential Archaeological Expressions of Nonbinary Gender in Pre-Contact Inuit Contexts

2019· article· en· W2976374911 on OpenAlexaffvenueabout
Meghan Walley

Bibliographic record

VenueÉtudes/Inuit/Studies · 2019
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsArchaeological recordDiversity (politics)Variety (cybernetics)ArchaeologyAnthropologySociologyHistoryComputer science

Abstract

fetched live from OpenAlex

In recent years, gender has factored heavily into the study of Inuit archaeological remains. Frequently, archaeologists have used diagnostic men’s and women’s tools to “see” gender in the archaeological record. However, recent anthropological literature attests to the existence of nonbinary gender categories in Inuit tradition. While the concept of nonbinary gender is not new in anthropological literature, it has not commonly been translated into meaningful archaeological research. Although many archaeologists studying Inuit gender have acknowledged the possibility of Inuit gender fluidity, virtually no archaeological research has directly addressed Inuit nonbinary gender. In this article, I discuss the anthropological concept of nonbinary gender and its diversity within Inuit culture, and then propose a variety of ways in which archaeologists conducting research on pre-contact Inuit gender might begin to study sites and materials within an interpretive framework that is more inclusive of these gender categories. These approaches include examination of artifacts, studies of the spatial distribution of sites, and re-examination of mortuary data. Through this work, I emphasize that gender occurs as a complex system rather than as two or three distinct sets of static social roles and that archaeologists need to adjust our approaches to past genders in order to see them through a culturally specific and meaningful lens.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.208
GPT teacher head0.413
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2019
Admission routes3
Has abstractyes

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