Epistemic Injustice In Library Work: A Case Study Of Patron-perpetrated Sexual Harassment
Bibliographic record
Abstract
We draw on the concept of epistemic injustice to understand (1) how library workers are harmed in their capacity as knowers when their experiences of patron-perpetrated sexual harassment (PPSH) are not believed; and (2) how a lack of hermeneutical resources—the conceptual knowledge to name and identify PPSH as gender-based violence—makes it difficult for them to make sense of their experiences. Library workers are subject to epistemic injustice when their knowledge/truth claims about PPSH are dismissed, diminished, disbelieved or otherwise discounted by patrons and people in the library organization, often in favour of the perpetrator’s account. Recognizing epistemic injustice within the context of PPSH can help the library workplace move towards justice.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.047 | 0.019 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.009 | 0.009 |
| 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, 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".