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Record W3031798493 · doi:10.29173/pathfinder15

Suppressing communities: An analysis of LGBTQ+ censorship in libraries

2020· article· en· W3031798493 on OpenAlexaffvenue
Taylor A. Stevens

Bibliographic record

VenuePathfinder A Canadian Journal for Information Science Students and Early Career Professionals · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCensorshipSociologyPolitical scienceInternet privacyLawPublic relationsComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.008
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.102
GPT teacher head0.373
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2020
Admission routes2
Has abstractyes

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