Reiterating Visibility: Canadian Librarians’ Experiences of Racial Microaggressions via Findings from a Minority Librarians Network Redux Survey
Bibliographic record
Abstract
Based on the data from the Visible Minority Librarians of Canada 2021 Redux Survey, this study examines experiences of racial microaggressions among visible minority librarians in Canada. This research fills the gap in the library and information science (LIS) literature regarding racial microaggressions in librarianship in the Canadian context. Of the 148 respondents, 69% (n=102) experienced at least one stated racial microaggression. The result of a Kruskal-Wallis H test revealed a significant association between years of experience as a librarian and a librarian’s overall experiences with microaggressions. A post hoc test based on Bonferroni correction was run, which indicated that librarians with less than five years of work experience encountered microaggressions less frequently compared to those with 11–15 years of experience. For the ten stated types of racial microaggressions, the most frequently reported type was “I was told that people of all racial groups face the same barriers in employment or promotion,” and the least frequently reported type was “A colleague claimed that he/she felt threatened because of my race.” Fisher's exact tests were further performed to examine how the respondents differed in their experiences of each microaggression. The test results revealed that the librarians with different personal attributes (ethnicity, disability status, gender identity, language used) and employment attributes (librarian experience, management position, library type) had significantly different encounters with eight forms of microaggression. Professional library associations and libraries must strengthen education about racial microaggressions and offer support to visible minority librarians when they are confronted with microaggressive behaviours.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".