Challenging Workplace Hostility: How Collective Action Can Address Sexual Harassment
Bibliographic record
Abstract
With millions of women sharing their experiences of harassment and abuse, the #MeToo movement confirmed what research had long shown: sexual harassment is a pervasive issue that disproportionately impacts women. Yet, despite the visibility of the #MeToo movement, which raised public awareness and prompted legislative and workplace policy changes, it appears that these efforts have had minimal impact on women’s experiences. This symposium brings together research that examines workplace sexual harassment from multiple perspectives and levels of analysis, specifically by examining how workers respond to harassment on an individual level, how organizations respond through social media campaigns, and the role of institutional interventions in facilitating policy change. Together, the included presentations offer important insights into addressing sexual harassment and promoting safer, more equitable workplaces. Compensating for Sexual Harassment: Gender and the Demand for Harassment-Free and Hybrid Workplaces Author: Manuela Collis; University of Toronto Author: Clementine Van Effenterre; University of Toronto (Not?) Tweeting Right through It: The Role of Gender in Justice Organizations’ Response to #MeToo Author: Michael Maffie; Cornell University Confronting Sexual Harassment in the Service Industry through Collective Bargaining Author: Emily E. LB. Twarog; University of Illinois at Urbana-Champaign Raising the Bar: Combating Sexual Harassment through Collective Bargaining Author: Shannon Potter; University of Toronto Author: Rachel Aleks; University of Windsor Author: Tina Saksida; University of Prince Edward Island
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.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".