A Historical Review of Gaslighting: Tracing Changing Conceptualizations Within Psychiatry and Psychology
Bibliographic record
Abstract
Gaslighting is a form of psychological manipulation that causes a victim to doubt their sense of reality, usually leading to a loss of agency and emotional and mental instability. The phenomenon was identified over 50 years ago and discourse on the topic was largely confined to psychiatry; however, interest in gaslighting has experienced a resurgence with expansion both in terms of the contexts in which gaslighting is thought to occur as well as disciplines weighing in on the topic. The aim of this article is to offer a historical review of work on gaslighting that tracks how the term has evolved and to identify core features of the phenomena. In doing so we identify points of consensus and tension in the literature. We also differentiate gaslighting from related constructs and conclude by making specific recommendations for the future of scholarship on gaslighting, aligning these proposals with clinical strategies to mitigate its psychological impact in therapeutic practice. • Conceptualizations of gaslighting has changed drastically since the 1960s. • Public and Academic interest in gaslighting is rising exponentially. • The fastest growing body of literature on gaslighting pertains to clinical and healthcare work. • The present work differentiates gaslighting from other related constructs. • Points of agreement and disagreement in the gaslighting literature are identified.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".