Bibliographic record
Abstract
Knowing one's place in the social order, whether that place is one of relative privilege or not, serves two psychologically ameliorative functions. It relieves one from the “anxiety of [gender] identity interrogation” and it helps to inform one as to the socially agreed upon, acceptable conduct for interpersonal exchanges--the episteme of social interaction. This Paper will demonstrate that gender identity is produced through relational, contextually influenced, interpretative processes. Because gender is constructed in societies which strongly embrace static, binary conceptions of gender, and in which social, familial, occupational, and sexual *139 interactions are heavily influenced by gendered social scripts, gender expressions which are ambiguous, or which have changed since a prior interaction, or which are strongly incongruent with normative understandings of the correlation between gender and biology, are typically experienced by others as at least uncomfortable, and often actually disruptive. The dominant social response to disruption is an ultimately futile effort to reinforce a gender binary. The law is frequently invoked in aid of this re-inscription of gender. In this Paper I argue that the disruption is produced by the binary model itself, and I propose legal strategies which will assist in a re-conceptualization of gender.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".