Sexual Misconduct in the Workplace: Organizational Consequences and the Role of Toxic Culture
Bibliographic record
Abstract
Despite increased attention following the #MeToo movement, sexual misconduct remains a persistent challenge in contemporary organizations. While important existing research has documented its impact on survivors, this symposium examines broader organizational implications and cultural dynamics that perpetuate misconduct. Through five presentations, we demonstrate how inadequate addressing of sexual misconduct affects firm value, performance, and talent acquisition, while exploring the reproduction of toxic workplace cultures. The first three presentations examine market reactions to punitive damages insurance, spillover effects on unit-level performance and innovation, and gendered responses to organizational policies among prospective employees. The final two presentations investigate cultural mechanisms, analyzing how hyper-competitive norms persist despite their negative effects on performance and well-being, and how organizational narratives shape silence culture around sexual harassment. Together, these papers challenge the prevalent organizational approach of containment over prevention, demonstrating its strategic limitations and cultural implications. Using diverse methodological approaches—from archival analyses, multi-methods, and synthetic control methods to survey experiments—this symposium advances our understanding of sexual misconduct beyond interpersonal dynamics to demonstrate its broader organizational consequences. Our findings suggest that addressing sexual misconduct effectively is not just an ethical imperative but a strategic necessity for organizational success and workplace equality. Value Implications of Insuring Against Punishment: Evidence from Court Precedents Author: Spencer Barnes; The University of Texas at El Paso Author: Marina Gertsberg; University of Melbourne Sexual Misconduct: Organizational Outcomes and Spillovers Author: Manuela Collis; University of Toronto Training vs termination:How men and women interpret org. strategies in response to sexual harassment Author: Elizabeth Lauren Campbell; University of California San Diego Author: Sae-Seul Park; Not Associated Norms at Work: Well-being, Performance and Hyper-Competition in Academia Author: Maria Guadalupe; INSEAD Author: Daisy Pollenne; INSEAD Sexual Harassment Stories and Silence Author: Olle Folke; Uppsala University Author: Andreas Kotsadam; - Author: Mette Løvgren; OsloMet – Oslo Metropolitan University Author: Johanna Rickne; Stockholm University
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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 | 0.013 | 0.037 |
| 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 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".