“Don’t You Love Me?” Abusers’ use of shame-to-guilt to coercively control 2SLGBTQQIA+ individuals and rural women experiencing intimate partner violence
Bibliographic record
Abstract
BACKGROUND: Abusers' use of manipulative behaviors to trigger feelings of shame-to-guilt (a process through which abusers shame their partners to incur feelings of guilt) among their 2SLGBTQQIA+ and rural women intimate partners is a type of emotional abuse used to coercively control their partners. OBJECTIVE: This study investigated the different tactics that abusers use to shame-to-guilt their partners who identify as 2SLGBTQQIA+ and/or reside in rural areas. DESIGN: A qualitative design was used to conduct this study. METHODS: = 24). RESULTS: Seven themes were identified based on participants' experiences, including shaming identity in relation to gender and sexual orientation (manifesting differentially between 2SLGBTQQ+ and rural women participants), emotional and sexual manipulation, threats of death by suicide (predominating among 2SLGBTQQ+ individuals), apologies and vacuous promises as components of the cycle of abuse, using one's parenting and children's well-being to manipulate partners, the use of health conditions and faking illness, and the use of religion or faith to reinforce gender standards. CONCLUSION: For 2SLGBTQQIA+ and rural women groups, situating shame-to-guilt behaviors within the cycle of abuse is important information that has not been explored extensively in the intimate partner violence literature. For individuals self-identifying as 2SLGBTQQIA+ or women living rurally, the means through which they are shamed-to-guilt intersects with their unique identities and positionality. Therefore, recommendations are presented to help these groups rebuild their identities when shame-to-guilt behaviors were experienced as part of the abusive dynamic.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".