Library Workers Experiencing or Observing Sexual Harassment in University of California Libraries is Commonplace and Commonly Unreported
Bibliographic record
Abstract
A Review of: Barr-Walker, J., Hoffner, C., McMunn-Tetangco, E., & Mody, N. (2021). Sexual harassment at University of California Libraries: Understanding the experiences of library staff members. College & Research Libraries, 82(2), 237. https://doi.org/10.5860/crl.82.2.237 Abstract Objective – To identify whether academic library workers at the University of California Libraries (UCL) system experienced or observed sexual harassment and to measure their reporting and disclosure behavior. Design – Anonymous online survey with open and closed-end questions. Setting – All UCL system campuses (Berkeley, Davis, Irvine, Los Angeles, Merced, Riverside, Santa Barbara, Santa Cruz, San Diego, and San Francisco). Subjects – All 1610 non-student employees working in UCL system were invited to participate, 579 (36%) responded. Methods – The authors engaged multiple stakeholder groups to refine and promote this census of UCL non-student workers. The survey was distributed via REDCap and remained open for six weeks of November to December 2018. All questions were optional. Certain demographic information was not collected because respondents might have been identified via deductive disclosure. The first author conducted descriptive statistical analysis and pairs of authors conducted thematic analysis. Main Results – More than half of respondents experienced or observed sexual harassment in the workplace; women were more likely to experience than observe and vice versa for men. Harassment was most likely to be exhibited by a coworker. Less than half of respondents felt that the UCL system administration considered the issue important. Nearly three out of every four respondents who had experienced harassment at work chose not to report or disclose; this did not vary significantly between women and men. Conclusion – Sexual harassment of library workers, often by other library workers, is widespread. Staff training and policies should incorporate the reality of gender harassment and commenting on a person's appearance—the two most common forms of harassment exhibited and observed.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".