A qualitative analysis of gaslighting in romantic relationships
Bibliographic record
Abstract
Abstract Gaslighting is an understudied form of abuse wherein a sane and rational survivor is convinced of their own epistemic incompetence on false pretenses by a perpetrator. The current study aimed to characterize the features of gaslighting as well as test and verify common claims about gaslighting. We recruited participants (N = 65) who self‐identified as having experienced gaslighting in romantic relationships to fill out a qualitative survey wherein they described instances of gaslighting, features of their relationships, and the consequences of gaslighting on their mental health. The age of participants in this study ranged from 18 to 69 (M = 29), most participants identified as female (48), and heterosexual (43). Gaslighting occurs within relationships that are typified by a combination of affectionate and abusive behaviors extended over the course of a relationship. Gaslighting victimization was associated with a diminished sense of self, mistrust of others, and on occasion, post‐traumatic growth. Those who recovered from gaslighting often emphasized the importance of separation from the perpetrator, prioritization of healthier relationships, and engaging in meaningful and re‐embodying activities. This study provides a basis for further research into gaslighting and recovery from gaslighting, which will contribute to the prevention and treatment for this type of abuse.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".