Genderfucking Non-Disclosure: Sexual Fraud,\nTransgender Bodies, and Messy Identities
Bibliographic record
Abstract
If I don't tell you that I was assigned male at birth, as a transgender person, can I go to jail for sexual assault by fraud? In some jurisdictionslike England or Israel, the answer is: yes. Previous arguments against this criminalisation have focused on the realness of trans people's genders: since trans men are men and trans women are women, it is not misleading for them to present as they do. Highlighting the limitationsofthis position, which doesn't fully account for the messiness ofgendered experiences, the author puts forward an argument against the criminalisation of (trans)gender history non-disclosure rooted in privacy. Gender identity is a private matter and people should not be forced to figure it out or communicate it to others to have an intimate life. Mobilised in this context, privacy can be understood as a refusal of the state's authority to order our gendered lives. The author argues that this mobilisation is compatible with leftist critiques ofprivacy. Finally, the author considers whether (trans)gender history non-disclosure is a criminal offence in Canada and concludes that it is not.
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 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.049 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".