Sexual Harassment Research at Work: Perspectives From Around the World
Bibliographic record
Abstract
Sexual harassment (SH) research has recently gone through a resurgence in practical relevance and scholarly interest. While advances have been made in understanding the nature of sexual harassment and its consequences for victims and organizations, much work is still needed to further understand the phenomenon from various cultural (i.e., countries) and work contexts (i.e., industry and job types). Through a collection of five papers representing data from seven countries, this symposium aims to broaden our understanding of SH by 1) examining contextual factors that facilitate, mitigate, and link SH with work outcomes, 2) uncovering similarities and differences in SH research findings from various cultural and industry contexts, and 3) highlighting actionable future research directions and practical evidence-based resolutions. This symposium also offers insights on current conceptual, methodological, and practical issues related to SH research as conducted in various parts of the world. Collectively, the five papers set the stage for further scholarship on SH to aid in the development of programs and policies to help mitigate its negative impact on individuals, teams, and organizations. Sexual harassment and silence: Getting in the way of healthier workplaces Author: Angela Workman-Stark; Athabasca U. Author: Ivana Vranjes; Tilburg U. Author: Zhanna Lyubykh; Beedie School of Business Simon Fraser U. Author: Sandy Hershcovis; U. of Calgary Author: Lilia Cortina; U. of Michigan Author: Carla Chrusch; U. of Calgary Author: Jennifer L. Berdahl; U. of British Columbia The role of anti-harassment policies amidst rising disgust and deviance after sexual harassment Author: Yijue Liang; George Mason U. Author: Tianjun Sun; Rice U. Author: Renee McCauley; George Mason U. Powerful yet powerless: Social media analysis of #MeToo cases in India and Pakistan Author: Imran Saqib; Alliance Manchester Business School, U. of Manchester Author: Aparna Gonibeed; Manchester Metropolitan U. Business School Unveiling the intersections of sexual violence and harassment among South Asian women in the UK Author: Saleema Kauser; Senior Lecturer U. of Manchester Author: Marianna Fotaki; Warwick Business School Sexual harassment in the world of work in Australia: A systematic review Author: Catherine Deen; U. of New South Wales Author: Sara Charlesworth; College of Business and Law, RMIT U.
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.027 | 0.033 |
| Scholarly communication | 0.037 | 0.020 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".