Developing the “Oppression-to-Incarceration Cycle” of Black American and First Nations Australian Trans Women: Applying the Intersectionality Research for Transgender Health Justice Framework
Bibliographic record
Abstract
Trans women are disproportionately incarcerated in the United States and Australia relative to the general population. Stark racial and ethnic disparities in incarceration rates mean that Black American and First Nations Australian trans women are overrepresented in incarceration relative to White and non-Indigenous cisgender and trans people. Informed by the Intersectionality Research for Transgender Health Justice (IRTHJ) framework, the current study drew upon lived experiences of Black American and First Nations Australian trans women to develop a conceptual model demonstrating how interlocking forces of oppression inform, maintain, and exacerbate pathways to incarceration and postrelease experiences. Using a flexible, iterative, and reflexive thematic analytic approach, we analyzed qualitative data from 12 semistructured interviews with formerly incarcerated trans women who had been incarcerated in sex-segregated male facilities. Three primary domains-pathways to incarceration, experiences during incarceration, and postrelease experiences-were used to develop the "oppression-to-incarceration cycle." This study represents a novel application of the IRTHJ framework that seeks to name intersecting power relations, disrupt the status quo, and center embodied knowledge in the lived realities of formerly incarcerated Black American and First Nations Australian trans women.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.004 |
| 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 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".