Coercive Control in 2SLGBTQQIA+ Relationships: A Scoping Review
Bibliographic record
Abstract
Existing measures and theories of intimate partner coercive control largely evaluate men's coercion of women. The extent of knowledge pertaining to intimate relationships among other genders and sexual identities is unclear. Guided by a theoretical framework of intersectionality, we examined and synthesized original studies on coercive control by (perpetration) or against (victimization) Two Spirit, lesbian, gay, bisexual, trans, queer, questioning, intersex, and asexual individuals within intimate partner relationships. We searched eight academic databases for records from 2014 through 2022 and hand-searched review articles' reference lists, supplemented with gray literature and website searches. Using duplicate screening, we identified 1,774 unique documents; 526 met preliminary eligibility criteria and 277 were retained for data extraction in duplicate. Coercive control was more common among minority individuals and was related to mental health challenges. Few studies reported on gender- or sexual-identity specific forms of coercive control, and an intersectional focus was uncommon. This review revealed a lack of agreed definition of coercive control or accepted standard of measurement, and a gap in research with individuals who identify as gender diverse, gender fluid or intersex, or those identifying their sexuality as asexual, pansexual, or sexually diverse.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".