Advancing Sexual Harassment Prevention and Elimination in the Sciences: “Every ... Health Organization Must Do Something Similar”
Bibliographic record
Abstract
BACKGROUND: Sexual harassment is pervasive in science. A 2018 report found that the prevalence of sexual harassment in academia in the United States is 58%. An activity held at an international scientific congress was designed to advance sexual harassment prevention and elimination and empower binary and nonbinary persons at risk for harassment, discrimination, and violence. The objective is to describe the activity and outcomes to provide a promising model for other scientific communities. METHODS: A description of the plenary and key components as well as the data collection and analysis of selected outcomes are provided. RESULTS: Among 1338 congress participants from 61 countries, 526 (39%) attended the #MeToo plenary, and the majority engaged in some way during the plenary session. Engagement included standing for the pledge (~85%), participating in the question and answer session (n = 5), seeking counseling (n = 3), and/or providing written post-it comments (n = 96). Respondents to a postcongress survey (n = 388 [24% of all attendees]) ranked the plenary as number 1 among 14 congressional plenaries. In postanalysis, the written post-it comments were sorted into 14 themes within 6 domains, including: (1) emotional responses, (2) barriers to speaking out, (3) public health priorities, (4) reframing narratives about the issue, (5) allyship, and (6) moving the issue forward. CONCLUSIONS: Scientific organizations, agencies, and institutions have an important role to play in setting norms and changing enabling policies toward a zero-tolerance culture of sexual harassment. The activity presented offers a promising model for scientific communities with similar goals. The outcomes suggest that the plenary successfully engaged participants and had a measurable impact on the participants.
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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".