Defining Gaslighting in Gender-Based Violence: A Mixed-Methods Systematic Review
Bibliographic record
Abstract
In both public and academic discourse, gaslighting has gained increased attention, especially regarding psychological abuse, power imbalance, and gender-based violence (GBV). However, the term gaslighting is often inconsistently defined and conflated with broader forms of manipulation. It is also largely examined in the context of intimate partner violence (IPV), which ignores its occurrence in other forms of GBV. The present study presents a systematic review that synthesizes interdisciplinary academic literature to create a comprehensive framework of gaslighting. This framework includes the specific tactics that are used by perpetrators of gaslighting, the social-psychological outcomes experienced by survivors, and the role of systemic inequalities and social power dynamics. A search across multiple databases identified 96 records that discussed gaslighting in relation to GBV. Thematic analysis revealed a two-part framework for understanding gaslighting: (a) gaslighting tactics, which were categorized into cognitive and perceptual manipulation, emotional and psychological abuse, power dynamics and control, and additional forms of manipulation and (b) survivor outcomes, including disruptions to perception and memory, emotional distress, social isolation, and resistance strategies. The findings show that gaslighting is more than just an interpersonal act; it is sustained within social structures, where perpetrators use identity factors and forms of marginalization to exploit survivors. Overall, this review presents a comprehensive definition of gaslighting that illustrates its epistemic nature and its intersection with systemic oppression. It is suggested that future research studies gaslighting in GBV contexts beyond IPV, while practice and policy efforts should seek to enhance recognition and support for survivors.
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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 | 0.026 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".