Exploring Potential Archaeological Expressions of Nonbinary Gender in Pre-Contact Inuit Contexts
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".