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Record W4381614789 · doi:10.23974/ijol.2023.vol8.2.279

Chinese Canadian Librarians

2023· article· en· W4381614789 on OpenAlexaffabout
Yanli Li

Bibliographic record

VenueInternational Journal of Librarianship · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsMentorshipRace (biology)Representation (politics)PerceptionWork (physics)PsychologyImmigrationEthnic groupUnderrepresented MinorityMedical educationPublic relationsSociologyPolitical scienceGender studiesMedicinePoliticsEngineering

Abstract

fetched live from OpenAlex

Based on the data from the Visible Minority Librarians of Canada Network (ViMLoC) 2021 Survey, this research examined the leadership roles held by Chinese Canadian librarians and their perceptions of inclusivity of work climate, job satisfaction, and race as a career barrier. Their encounters with racial microaggressions and mentorship experience were also explored. Of the 38 respondents, 79% (n=30) were in non-management positions. 82% (n=31) felt very satisfied or satisfied with their jobs; however, their work climate was not found to be inclusive to all respondents. 87% (n=33) indicated that race was a barrier to their career. Fisher’s exact tests were run to compare the management group and non-management group. The results showed that those in management positions were less satisfied with their jobs and less likely to feel free to express their views openly. They experienced various forms of racial microaggressions. Nearly half of the respondents sought mentoring support and two-thirds of them found mentorship extremely helpful or very helpful. To increase representation in librarianship, library school students and first-generation immigrants of Chinese descent need to be inspired to enter librarianship and reach higher professional goals. Chinese librarians would benefit from leadership programs tailored to minority librarians in Canada.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.005
Open science0.0010.000
Research integrity0.0000.000
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 head0.045
GPT teacher head0.342
Teacher spread0.297 · 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 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

Citations2
Published2023
Admission routes2
Has abstractyes

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