Challenging Eurocentrism: Applying critical theory and anti-oppression frameworks to EDI work in libraries
Bibliographic record
Abstract
In this IDEAL session, preliminary findings from a Canadian Association of Research Libraries (CARL) funded project on applying critical theory and anti-oppression frameworks to EDI-related work in libraries (committees, hiring practices, internship opportunities, etc.) were shared. During and post COVID-19, and catapulted by the racial violences in North America, a racial awakening in libraries occurred and many libraries began to prioritize anti-oppression and anti-racism efforts. Solidarity statements were shared, resource lists were created, read, and discussed, and new EDI related programs and positions created to lead EDI work on campuses. However, existing EDI epistemologies, values and practices are still rooted in and prefer to launch from Eurocentric paradigms. In this session, researchers shared details of their work (methodology, data collection and analysis, and preliminary findings). Researchers used appreciative inquiry in their data analysis and provided some examples of data in the presentation. Early findings show that libraries must resist the urge to establish EDI efforts through Eurocentric paradigms.
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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 | 0.086 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.036 | 0.073 |
| Scholarly communication | 0.032 | 0.026 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".