Bibliographic record
Abstract
Library professionals, often without knowing, can indirectly reduce access to information, a phenomenon known as soft censorship. This research-in-progress conceptualizes key aspects of soft censorship and identifies conditions conducive to soft censorship in libraries. The individual and systematic biases that form the cycles that facilitate soft censorship become evident through this conceptualization, including content warnings, neutrality, subject headings, hiring, and publishing. In future work, we will analyze other areas where soft censorship has the potential to proliferate, including acquisitions, weeding, and reference, to obtain a fuller picture of soft censorship. Les cycles du biais : la censure subtile dans les bibliothèques RésuméLes bibliothécaires, souvent sans le savoir, peuvent indirectement réduire l'accès à l'information, un phénomène connu sous le nom de censure subtile. Cette recherche en cours conceptualise les aspects clés de la censure subtile et identifie les conditions favorables à celle-ci dans les bibliothèques. Les biais individuels et systémiques qui forment les cycles facilitant la censure subtile deviennent évidents à travers la conceptualisation, en incluant les avertissements de contenu, la neutralité, les titres et sous-titres, l'embauche et la publication. Dans un projet de recherche futur, nous analyserons d'autres sphères où la censure subtile a le potentiel de proliférer, en incluant les acquisitions, la pré-sélection et la référence, afin d'obtenir une image plus complète de la censure subtile. Mots-clésCensure; Censure subtile; Biais implicite; Bibliothèques; Science de l'information
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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 | 0.013 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".