Sex-based harassment and organizational silencing: How women are led to reluctant acquiescence in academia
Bibliographic record
Abstract
The #MeToo and the Time’s Up movements have raised the issue of sexual harassment encountered by women to the level of public consciousness. Together, these movements have captured not only the ubiquity of sexual harassment in the everyday functioning of the workplace, but they have also demonstrated how women are silenced about their experiences of it. Inspired by the political and the social currents emerging from these movements, and theoretically informed by ideas of discursive hegemony, rhetorical persuasion and affective practice, this article draws on a qualitative study of early- and mid-career female academics in business schools to answer the following question: How are victims who start to voice their experiences of sex-based harassment silenced within the workplace? Our findings reveal that organizational silence is the product of various third-party actors (e.g. line managers, HR, colleagues) who mobilize myriad discourses to persuade victims not to voice their discontent. We develop the concept of ‘reluctant acquiescence’ to explain the victims’ response to organizational silencing. In terms of its contributions to the extant literature, this article: (i) moves away from explanations of sex-based harassment that focus solely (or predominately) on the actions of individual perpetrators; and (ii) shows how reluctant acquiescence leads to maintaining the status quo in the organization. In highlighting features of academic work that facilitate reluctant acquiescence, we call for more contextualization of the dynamics of sex-based harassment specifically, and other forms of workplace mistreatment broadly.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".