“Finding a Way To Say ‘No’”: Library Employees' Responses to Sexual Harassment as Emotional Labour
Bibliographic record
Abstract
ABSTRACT Patron‐perpetrated sexual harassment (PPSH) is a form of gender‐based violence and a pervasive problem in libraries. However, contending with PPSH requires the performance of emotional labour by library workers because of workplace cultures and professional values that prioritize patron and institutional comfort. To better understand library workers' emotional labour as they respond to PPSH, we analyzed 510 survey responses where participants shared their experiences of, their responses to, and feelings about, PPSH. Three responsive strategies emerged: acceptance, indirect refusal, or direct refusal. Overwhelmingly, library workers reported negative emotions about the incidents. Despite these negative feelings, library workers consistently responded to PPSH by performing emotional labour that upheld “polite and professional” values. Our findings raise concerning questions for the field of library and information studies about the implicit and explicit expectations placed on library workers to perform emotional labour in response to PPSH, particularly within the context of a feminized profession and with the knowledge that PPSH harms library workers. Our goal is to support library workers and their institutions to “find a way to say ‘no’” to gender‐based violence in the workplace.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".