See no evil, hear no evil, speak no evil: Theorizing network silence around sexual harassment.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it