On the Structure and Nomological Network of Gaslighting in the Workplace - A Leadership Perspective
Bibliographic record
Abstract
In recent years, the term gaslighting has become popular for describing deliberate attempts to undermine another person’s reality by making them feel “crazy.” Although it has mostly been studied in romantic relationships, recent work suggests that gaslighting occurs in other contexts where power imbalance exists. The current research aims to a) conceptualize gaslighting within the leader-employee relationship, b) differentiate between leaders’ use of gaslighting tactics and employees’ psychological experience of being gaslighted, c) develop and validate measures to captures these two constructs, and d) develop and test a comprehensive nomological network around them. First, we adapted and developed items based on a review of the broader gaslighting literature. Next, we tested the psychometric properties of our measures in two working adult samples (N = 314 and N = 398) and provide initial evidence of convergent and discriminant validity. Finally, we examine the proposed nomological network in a time-lagged study of 632 employees and 194 supervisors. The results show that employee experienced gaslighting state mediates the adverse effect of leader gaslighting tactics on self-focused (e.g., organizational-based self-esteem), performance-focused (e.g., task performance), supervisor-focused (e.g., supervisor-directed impression management), and coworker-focused (e.g., ostracism by coworkers) outcomes. Many of these indirect effects were stronger when the employee reported higher (versus lower) LMX relationship quality with the leader. We conclude with a discussion of the theoretical and practical implications of these findings.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".