Partner Violence in Transgender Communities: What Helping Professionals Need to Know
Bibliographic record
Abstract
In recent decades, there has been an increase in scholarly attention devoted to the study of intimate partner violence (IPV) within rainbow communities. While a growing body of scholarship now informs our understanding of the experiences of gay, lesbian, and bisexual men and women with IPV, comparatively less is known about IPV within transgender communities. Drawing on the published literature on transgender intimate relationships, this article seeks to provide practitioners with a foundational understanding of IPV in the lives of transgender people. Specifically, we will examine (a) methodological, political, and social barriers to the creation of knowledge about transgender IPV; (b) the familial and relationship contexts of IPV within transgender communities; (c) the prevalence of IPV experienced by transgender survivors; (d) the dynamics of IPV perpetrated against and/or by transgender persons; (e) the problematic use of the “trans panic” defense by perpetrators of IPV in legal contexts; and (f) recommendations for supporting transgender survivors of IPV in ways that are trans-inclusive and trans-positive.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.011 | 0.025 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.020 | 0.023 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".