Networks of complicity: social networks and sex harassment
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the question of why sex harassment persists in organizations for prolonged periods – often as an open secret. Design/methodology/approach In-depth interviews were conducted with 28 people in diverse organizations experiencing persistent sex harassment. Data were analyzed using standard qualitative methods. Findings The overarching finding was that perpetrators were embedded in networks of complicity that were central to explaining the persistence of sex harassment in organizations. By using power and manipulating information, perpetrators built networks that protected them from sanction and enabled their behavior to continue unchecked. Networks of complicity metastasized and caused lasting harm to victims, other employees and the organization as a whole. Research limitations/implications The authors used broad, open-ended questions and guided introspection to guard against the tendency to ask for information to confirm their assumptions, and the authors analyzed the data independently to mitigate subjectivity and establish reliability. Practical implications To stop persistent sex harassment, not only must perpetrators be removed, but formal and informal ties among network of complicity members must also be weakened or broken, and victims must be integrated into networks of support. Bystanders must be trained and activated to take positive action, and power must be diffused through egalitarian leadership. Social implications Understanding the power of networks in enabling perpetrators to persist in their destructive behavior is another step in countering sex harassment. Originality/value Social network theory has rarely been used to understand sex harassment or why it persists.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".