Suppressing communities: An analysis of LGBTQ+ censorship in libraries
Bibliographic record
Abstract
Librarians serve as defenders of intellectual freedom and social responsibility, and this includes speaking out against censorship. Censorship of information, materials, and books occurs in the public, but censorship can also occur in libraries themselves. Those impacted the most by this censorship are marginalized communities, such as the LGBTQ+ community. The purpose of this paper is to explore how internal, external and institutional censorship affects the LGBTQ+ community and what librarians can do to uphold their defense against censorship. Internal, or self-censorship, occurs at the librarian level where LGBTQ+ materials may be hidden by librarians or library staff or simply not ordered due to pressure from the community the library serves. External censorship occurs at the community level where the community culture pushes for the censorship of LGBTQ+ materials. Lastly, institutional censorship occurs at the classification level where classification models such as the Dewey Decimal System or subject headings may not provide accurate representation for LGBTQ+ materials. In order to put an end to these forms of censorship, trained and certified librarians must act as agents of change, committing to their due diligence to provide information to all members of their communities.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".