Bibliographic record
Abstract
In this article, I extend the feminist use of Friedrich Nietzsche’s account of memory and forgetting to consider the contemporary externalization of memory foregrounded by transgender experience. Nietzsche’s On the Genealogy of Morals argues that memory is “burnt in” to the forgetful body as a necessary part of subject-formation and the requirements of a social order. Feminist philosophers have employed Nietzsche’s account to illuminate how gender, as memory, becomes embodied. While the account of the “burnt in” repetitions of gender allows us to theorize processes of embodied identity on an individual level, analyzing gender today requires also accounting for how gender is externalized. I take up this question through the specific examples of identity documents and sex-segregated bathrooms. Returning to Nietzsche’s call to practice a resistant forgetting, I conclude by exploring the distinct strategies required to disrupt externalized memory. These strategies include contesting the use of past gender assignments in data collection and rewriting architectural reminders 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".