Bibliographic record
Abstract
Gender norms can guide our sense of what we feel like we ought to do, even when we don't want them to. Understanding this norm responsiveness is an important part of understanding how oppressive gender systems are sustained. According to a social constructionist position, gender norm responsiveness happens as a result of social training, or socialization. It's often assumed that this training depends on our gender categories—that, for example, those who occupy the category “man” will be responsive to masculine norms, and so on. Call this a category-based view. But trans and gender-nonconforming people are often responsive to gender norms that don't match our gender categories. This is sometimes taken as evidence for the conclusion that normative masculinity and femininity are somehow innate or presocialized. I reject this inference. I argue that we should instead dispense with a category-based view, and instead adopt a traits-based view. Gender norms apply to individuals on the basis of the gender-coded traits that they express. A traits-based view represents a social constructionist account of gender norms that leaves room for trans and GNC experiences of normative gender, and thus represents an important step towards creating inclusive theory.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".