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Record W2955702879 · doi:10.7152/acro.v29i1.15449

Contextual Classification at Out On The Shelves Library

2019· article· en· W2955702879 on OpenAlexaffabout
Amber Dierking

Bibliographic record

VenueAdvances in Classification Research Online · 2019
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsQueerTransgenderLesbianSituatedComputer scienceSociologyLibrary classificationWorld Wide WebPublic relationsArtificial intelligenceGender studiesPolitical science

Abstract

fetched live from OpenAlex

Out On The Shelves is Vancouver’s only Lesbian, Gay, Bisexual, Transgender, Two-Spirit, Queer, Intersex, Aromantic/Asexual (LGBT2QIA+) library. Due to recent organizational changes, it has become apparent that its current classification system is no longer working effectively. Not only was the previous system unstructured and confusing, it failed to explicitly represent many aspects of the community it serves. This project was undertaken during the summer of 2018, researching alternative classification and queer issues in knowledge organization to determine how to improve it. This research, combined with careful consideration of the needs of the library itself and its users, suggested that building a local, contextually-situated, classification system would be best. A new classification system has been built for Out On The Shelves Library, and will be implemented in several stages beginning in October 2018, with the end goal to be finished by the end of December 2018. The new system intends to be living and changeable, one that lays bare its structures and decisionmaking processes while centering and celebrating the LGBT2QIA+ community and working within the realities of being a small, unfunded, volunteer-run, public library.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.472
Threshold uncertainty score0.939

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0120.002
Scholarly communication0.0130.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0330.005

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 head0.248
GPT teacher head0.521
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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Same venueAdvances in Classification Research OnlineSame topicLGBTQ Health, Identity, and PolicyFrench-language works237,207