What Will End the Silence? Understanding Employee Voice in Cases of Workplace Sexual Misconduct
Bibliographic record
Abstract
Employee voice has been identified as an integral means of addressing workplace sexual misconduct, yet little is known of the steps organizations can take to promote reporting of these incidents. The current study investigated predictors of target and observer reporting among 3,230 gender harassment, 890 sexual advance harassment, and 570 sexual assault incidents within an organization actively seeking to reduce sexual misconduct prevalence. After controlling for known predictors of employee voice in cases of mistreatment, including factors related to the target (sex, race, tenure) and the incident (frequency of sexual misconduct, past sexual misconduct experience, relative perpetrator power), mutable organizational factors contributed to the prediction of target voice across sexual misconduct contexts. However, the directionality of these effects-including counterintuitive findings related to targets' perceptions of organizational intolerance and reporting trends among visible minority and LGBT targets-incite a nuanced discussion of implications pertaining to target and observer voice.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".