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Designing the Diversity of Canadian Libraries: Excerpts from the CARL Inclusion Perspectives Webinar by Racialized Library Colleagues

2022· article· en· W4311236721 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePartnership The Canadian Journal of Library and Information Practice and Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsRoyal Saskatchewan MuseumUniversity of SaskatchewanUniversity of OttawaUniversity of ManitobaToronto Metropolitan UniversityLibrary and Archives CanadaUniversity of British Columbia
Fundersnot available
KeywordsInclusion (mineral)Diversity (politics)Library scienceEquity (law)SociologyRacismAcademic libraryAccreditationAdvice (programming)Political scienceMedia studiesPublic relationsGender studiesLawAnthropologyComputer science

Abstract

fetched live from OpenAlex

Five academic librarians from libraries that represent the Canadian Academic Research Libraries (CARL) were invited to share their experiences as racialized librarians. In 2021, the Canadian Academic Research Libraries (CARL) hosted an Inclusion Perspectives Webinar Series, organized by CARL’s Equity, Diversity, and Inclusion Working Group (EDIWG) and the contents of this paper are presentations by these librarians who were invited to speak on systems, structures, and policies needed to dismantle racism; practical strategies to attract and retain racialized library employees; accreditation issues; and provide advice for what Canadian library leaders can start doing immediately.

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.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.787
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0160.001
Scholarly communication0.0010.032
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.330
Teacher spread0.254 · 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