Bibliographic record
Abstract
Abstract In this commentary, we explore the implications of the 2024 US elections on trans lives. While we focus on the United States, we situate anti‐trans politics within emerging fascist movements around the world, as the elections have impacts beyond national borders. We examine four areas of impact: the body, public space, legal geographies and mobilities. Trans healthcare and bodily autonomy are under attack in a myriad of legal and legislative venues across the United States, with devastating consequences upon the health and well‐being of trans people, especially youth. These attacks are complemented by legislation that criminalises trans existence in public space, including renewed ‘bathroom bans’ and bans on drag and other gender non‐conforming performances. Undergirding all of these efforts is a fascist attempt to ‘eradicate transgenderism from public life’ in order to safeguard the ‘purity’ of the fascist body politic. This imperative is evident in emergent legal geographies of trans lives, which are increasingly imperiled by the denial of state identification and documentation. Ironically, trans mobility depends on these volatile regimes of legal recognition, and renewed anti‐trans politics perniciously constricts the capacity of trans people to flee jurisdictions where they are being legislated out of existence. We conclude with a brief meditation about what resurgent gender fascism means for the discipline of Geography, arguing that geographers have an ethical and intellectual obligation to protect trans lives and resist fascism.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".