Policing and Carcerality in Public Libraries
Bibliographic record
Abstract
Policing in libraries poses a significant barrier to access for many potential patrons—and yet, much of the literature on library security advocates for strong security measures without regard for the safety and wellbeing of patrons and staff who are BIPOC, LGBTQ2SIA+, disabled, and unhoused. Considerable lived experience and research shows that police disproportionately target those whose identities deviate from the norm, but in a society with so many diverse experiences, identities, and relationships to power and authority, we are obligated as socially conscious library workers to consider how our relationships to policing and security in the library affect all peoples. To do so, we draw on an intersectional abolitionist praxis that seeks to deconstruct the carceral and penal systems omnipresent in our society. After exploring some of the security measures taken by public libraries that enable the carceral state, we propose alternative measures that can be taken through the acronym-based catchphrase to encourage library workers of all types to give greater consideration to the ramifications of involving police in difficult patron interactions.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.040 | 0.045 |
| Scholarly communication | 0.027 | 0.011 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".