See no evil, hear no evil, speak no evil: Theorizing network silence around sexual harassment.
Bibliographic record
Abstract
#MeToo has inspired the voices of millions of people (mostly women) to speak up about sexual harassment at work. The high-profile cases that reignited this movement have revealed that sexual harassment is and has been shrouded in silence, sometimes for decades. In the face of sexual harassment, managers, witnesses and targets often remain silent, wittingly or unwittingly protecting perpetrators and allowing harassment to persist. In this integrated conceptual review, we introduce the concept of network silence around sexual harassment, and theorize that social network compositions and belief systems can promote network silence. Specifically, network composition (harasser and male centrality) and belief systems (harassment myths and valorizing masculinity) combine to instill network silence around sexual harassment. Moreover, such belief systems elevate harassers and men to central positions within networks, who in turn may promote problematic belief systems, creating a mutually reinforcing dynamic. We theorize that network silence contributes to the persistence of sexual harassment due to the lack of consequences for perpetrators and support for victims, which further reinforces silence. Collectively, this process generates a culture of sexual harassment. We identify ways that organizations can employ an understanding of social networks to intervene in the social forces that give rise to silence surrounding sexual harassment. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".