Revealing the Invisible Cage: Understanding Coercive Control Through the Eyes of Survivors in the Era of COVID-19
Bibliographic record
Abstract
Coercive control (CC) is a pervasive, systematic pattern of behaviors used by an intimate partner to exert power over, manipulate, intimidate, control, and undermine a victim's ability to leave the relationship. Compared to intimate partner violence (IPV) involving physical violence alone, abusive relationships marked by CC are linked with more long-lasting psychological consequences and an increased risk for more severe physical injuries, including domestic homicide. Dutton and Goodman (2005) created a theoretical model outlining distinct and interrelated components involved in the development and maintenance of coercively controlling relationships. In this qualitative study, I investigated whether components of this model reflect survivors' lived experiences of CC. I also examined survivors' experiences of physical IPV and CC broadly, the sequence in which CC and physical IPV occur in, and the impact of the COVID-19 pandemic on survivors' experiences of IPV. Trauma-informed interviews were completed individually with 12 Canadian women (age range, 23-56; M = 39.8) accessing women's shelters. Transcripts were analyzed using Braun and Clarke's (2021) reflexive thematic analysis. Themes described survivors' lived experiences of sexual coercion and CC marked by pervasive, frequent, ongoing patterns of CC that deprived women of freedom. Components of Dutton and Goodman's (2005) model of CC were captured, including grooming methods and the use of demands, threats, and surveillance as coercion tactics. Themes described CC preceding physical IPV or both forms of IPV emerging together early in relationships. Experiences of IPV during the COVID-19 pandemic encompassed pandemic restrictions facilitating CC, reduced opportunities for survivors to leave or seek support, and increases in physical IPV connected to increased isolation. These findings have implications for partner aggression research, prevention strategies, and educational initiatives aimed at reducing IPV in romantic relationships.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| 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".