MétaCan
Menu
Back to cohort
Record W4387252721 · doi:10.21900/j.alise.2023.1366

Advancing Anti-Racism in Public Libraries for Black Youth in Canada

2023· article· en· W4387252721 on OpenAlexaffabout
Amber Matthews

Bibliographic record

VenueProceedings of the ALISE Annual Conference · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsWestern University
Fundersnot available
KeywordsInclusion (mineral)RacismOutreachYouth engagementSociologyPerformative utterancePublic relationsIdentity (music)Political scienceGender studies

Abstract

fetched live from OpenAlex

A critical community-based study exploring Black youth experiences in Canadian public libraries and community-based youth programs. Participants were youth, aged 13 to 24, in London that do not often use public libraries and parents of Black youth. Data were drawn from semi-structured interviews with youth and caregivers. An arts-based qualitative tool was also used with youth as an age-appropriate method of expression and verification. The study sought to: understand why some youth use community-based programs instead of libraries and if this relates to experiences or perceptions of anti-Black racism; identify programs that help youth navigate structural challenges and opportunities for libraries to support them; and understand what motivates youth and caregivers to seek library and/or community-based programs. Public libraries were identified as a safe community space and youth feel comfortable visiting and using library services. However, they identify structural concerns (e.g., lack of belonging, performative inclusion, etc.) as barriers to participation. Black representation, identity-affirming programs, and motivational staff are key recommendations for public libraries that arise from this study.

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)0.002
metaresearch head score (Gemma)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.381

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0270.005
Scholarly communication0.0050.001
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.047
GPT teacher head0.275
Teacher spread0.228 · 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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the ALISE Annual ConferenceSame topicLibrary Science and AdministrationFrench-language works237,207