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Record W4281488510 · doi:10.1080/01616846.2022.2074244

“Everybody Thinks Public Libraries Have Only Books”: Public Library Usage and Settlement of Bangladeshi Immigrants in Canada

2022· article· en· W4281488510 on OpenAlexaffabout
Nafiz Zaman Shuva

Bibliographic record

VenuePublic Library Quarterly · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsWestern University
Fundersnot available
KeywordsOutreachSettlement (finance)ImmigrationEthnic groupContext (archaeology)Political sciencePublic relationsSociologyEconomic growthBusinessGeographyLawEconomics

Abstract

fetched live from OpenAlex

Many public libraries in Canada offer services and programs for immigrants, including employment assistance to assist newcomers with their settlement in Canada. Using a mixed method research design, this study explores the use of public libraries by Bangladeshi immigrants in Canada including their use of public library settlement services, along with their pre-migration access to public library services. The study finds immigrants’ use of public libraries declines over time. However, it is also evident in this study that public libraries played a positive role in newcomers’ settlement into Canadian society. The findings related to the lack of familiarity with public libraries in a pre-arrival context highlight the importance of having a strong public library outreach program for immigrant populations. The author urges public libraries offering services to immigrants to make meaningful partnerships with pre-and post-arrival settlement agencies, local ethnic community and religious organizations, and ethnic media to spread the word about public library programs and services for immigrants.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.230

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0160.004
Scholarly communication0.0050.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.021
GPT teacher head0.222
Teacher spread0.202 · 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 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

Citations17
Published2022
Admission routes2
Has abstractyes

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Same venuePublic Library QuarterlySame topicLibrary Science and AdministrationFrench-language works237,207