Unpacking Anti‐Transgender Bias: Leveraging Mechanisms From Person Perception
Bibliographic record
Abstract
ABSTRACT Anti‐transgender bias is an important area of research given the disproportionate negative outcomes transgender relative to cisgender individuals experience. Yet, the mechanisms underlying this disproportionate negative bias remain understudied. We posit that a person perception perspective could be particularly beneficial to characterize mechanisms for this negative bias experienced by transgender individuals. We hypothesize that slower or more effortful processing when processing the gender of transgender versus cisgender individuals contributes to discrimination against transgender individuals (e.g., misgendering and degendering). We introduce and discuss two forms of potentially disfluent gender processing, conceptual and perceptual, and explore their potential connections to these biases. We also suggest how the existing literature on person perception can guide future research on the mechanisms driving anti‐transgender bias and, consequently, potentially inform interventions to reduce bias against the transgender community.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".