A Descriptive Intersectional Analysis of Anticipated Discrimination in Transgender and Non-Binary People in Canada
Bibliographic record
Abstract
Background: Anticipating discrimination may lead to poor physical and mental health outcomes for transgender and nonbinary (TGNB) individuals. It is important to take an intersectional approach to understand how anticipated discrimination is unequally distributed among TGNB Canadians.\nMethods: Among a Canadian community sample of TGNB people aged ≥14 years, the Intersectional Discrimination Index – Anticipated discrimination (InDI-A) measure was validated. Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) and multiple linear regression were used to compare mean anticipated discrimination across ethnoracial and sex/gender intersections.\nResults: A one-factor model of the InDI-A was supported with a satisfactory fit. Racialized participants, trans women, and nonbinary people assigned male at birth experienced higher mean anticipated discrimination. There were no differences between the levels of anticipated discrimination for Black and non-Black racialized participants.\nConclusion: There is a little variation between intersections, but all intersections experienced high levels of mean anticipated discrimination.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".