Libraries in the Spotlight: First Amendment Auditors and Social Media Commentary
Bibliographic record
Abstract
Social media platforms have amplified public scrutiny of libraries, as First Amendment auditors (FAAs) record their interactions to test constitutional boundaries. These encounters spark debates over the balance between free speech and maintaining an inclusive, orderly environment. Through an analysis of 300 YouTube comments on FAA-library interactions, this study highlights polarized public reactions influenced by selective video editing and algorithm-driven echo chambers. While auditors bring attention to issues of transparency, their confrontational tactics often challenge the core mission of libraries. The findings emphasize the importance of clear policies, staff training, and strategies to address the complexities of digital accountability. Les bibliothèques sous les projecteurs: auditeurs du Premier amendement et commentaires sur les médias sociaux RésuméLes plateformes de médias sociaux ont amplifié l'examen public des bibliothèques, car les auditeurs du Premier amendement enregistrent leurs interactions pour tester les limites constitutionnelles. Ces rencontres suscitent des débats sur l'équilibre entre la liberté d'expression et le maintien d'un environnement inclusif et ordonné. Grâce à l'analyse de 300 commentaires YouTube sur les interactions entre les auditeurs et les bibliothèques, cette étude met en lumière les réactions polarisées du public qui sont influencées par le montage de vidéo sélectif et les algorithmes. Alors que les auditeurs attirent l'attention sur les questions de transparence, leurs tactiques de confrontation remettent souvent en question la mission principale des bibliothèques. Les résultats soulignent l'importance de politiques claires, de formation du personnel et de stratégies pour adresser les complexités de la responsabilité numérique.
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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 | 0.047 | 0.190 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.022 | 0.021 |
| Scholarly communication | 0.028 | 0.017 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.008 |
| 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".