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Record W2118998423 · doi:10.18438/b8zc88

Identifying the Visible Minority Librarians in Canada: A National Survey

2015· article· en· W2118998423 on OpenAlexaffvenueabout
Maha Kumaran, Heather Cai

Bibliographic record

VenueEvidence Based Library and Information Practice · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsDemographicsMulticulturalismLibrary scienceMedical educationPsychologyPublic relationsPolitical scienceSociologyMedicineComputer sciencePedagogy

Abstract

fetched live from OpenAlex

Abstract Objective – This paper is based on a national survey conducted in late 2013 by the authors, then co-moderators of the Visible Minority Librarians of Canada (ViMLoC) Network of the Canadian Library Association (CLA). It is a first survey of its kind, aiming to capture a snapshot of the demographics of the visible minority librarians working in Canadian institutions. The authors hoped that the data collected from the survey and the analysis presented in this paper would help identify the needs, challenges and barriers of this group of librarians and set future directions for ViMLoC. The authors also hoped that the findings would be useful to library administrators, librarians, and researchers working on multicultural issues, diversity, recruitment and retention, leadership, library management, and other related areas. Methods – An online survey questionnaire was created and the survey invitation was sent to visible minority librarians through relevant library association electronic mail lists and posted on ViMLoC’s electronic mail list and website. The survey consisted of 12 questions: multiple-choice, yes/no questions, and open-ended. The survey asked if the participants were visible minority librarians. If they responded “No,” the survey closed for them. Respondents who did not identify themselves as minority librarians were excluded from completing the survey. Results – Of the 192 individuals that attempted, 120 who identified themselves as visible minority librarians completed the survey. Of these, 36% identified themselves as Chinese, followed by South Asian (20%) and Black (12%). There were 63% who identified themselves as first generation visible minorities and 28% who identified themselves as second generation. A total of 84% completed their library degree in Canada. Equal numbers (38% each) identified themselves as working in public and academic libraries, followed by 15% in special libraries. Although they are spread out all over Canada and beyond, a vast majority of them are in British Columbia (40%) and Ontario (26%). There were 38% who identified themselves as reference/information services librarians, followed by “other” (18%) and “liaison librarian” (17%). A total of 82% responded that they worked full time. The open-ended question at the end of the survey was answered by 42.5% of the respondents, with responses falling within the following broad themes: jobs, mentorship, professional development courses, workplace issues, general barriers, and success stories. Conclusions – There are at least 120 first, second, and other generation minority librarians working in (or for) Canadian institutions across the country and beyond. They work in different kinds of libraries, are spread out all over Canada, and have had their library education in various countries or in Canada. They need a forum to discuss their issues and to have networking opportunities, and a mentorship program to seek advice from other librarians with similar backgrounds who have been in similar situations to themselves when finding jobs or re-pursuing their professional library degrees. Getting support from and working collaboratively with CLA, ViMLoC can be proactive in helping this group of visible minority librarians.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.029
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0070.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.089
GPT teacher head0.324
Teacher spread0.236 · 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

Citations15
Published2015
Admission routes3
Has abstractyes

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