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Record W4410705315 · doi:10.29173/cais1946

Why Should I Stay?

2025· article· en· W4410705315 on OpenAlexvenueaboutno aff
Amber Matthews

Bibliographic record

VenueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Substance Use and School Attendance
Canadian institutionsnot available
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

This study used narrative inquiry and critical approaches to race to explore Black youth perceptions of public libraries and community-based programs. The study was conducted in Ontario with youth aged 13 to 24 and parents of youth. Data was drawn from semi-structured interviews with youth and parents and an arts-based qualitative tool. Libraries were identified as safe and welcoming community spaces. However, youth feel poorly represented and seek youth programs with a race-conscious and inclusive approach. Core recommendations include equitable approaches to representation, strengthening relationships with partner organizations, and addressing performative approaches to inclusion. « Pourquoi devrais-je rester? » Les jeunes canadiens noirs et les bibliothèques publiques RésuméCette étude a employé l'enquête descriptive ainsi qu'une approche critique par rapport à l'ethnicité afin d'explorer les perceptions des bibliothèques publiques et des programmes communautaires qu'ont les jeunes noirs. L'étude a été faite en Ontario avec des jeunes âgés de 13 à 24 ans, ainsi qu'avec leurs parents. Les données ont été extraites d'entrevues semi-structurées avec les jeunes et leurs parents et d'un outil artistique de nature qualitative. Les bibliothèques ont été identifiées comme étant des espaces communautaires sécuritaires et accueillants. Toutefois, les jeunes se sentent peu représentés et recherchent des programmes pour les jeunes qui prennent en compte les différentes ethnicités et qui adoptent une approche inclusive.

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)0.001
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0020.006
Open science0.0020.000
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 head0.037
GPT teacher head0.300
Teacher spread0.262 · 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 designNot applicable
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
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSISame topicYouth Substance Use and School AttendanceFrench-language works237,207