EQUITY, DIVERSITY, INCLUSION, AND DECOLONIZATION (EDID) INITIATIVES IN THE COLLECTION MANAGEMENT PRACTICES OF CANADIAN UNIVERSITY LIBRARIES: PERFORMATIVE OR ACTION-BASED COMMITMENTS?
Bibliographic record
Abstract
Canadian university libraries support the teaching, learning, research and community engagement activities of their parent institutions using collections of information resources in diverse disciplines and formats. Despite the continuous acceptance of digital and print collections and the steady increase in the amount spent on these resources, the representation of equity-deserving groups in Canadian university libraries’ collections remains low. In this doctoral study I investigate how the collection management practices, and Equity, Diversity, Inclusion and Decolonization (EDID) initiatives of Canadian university libraries could address diversity issues and the limited representation of equity-deserving groups in their collections. It uses a qualitative research design involving policy document analysis and qualitative interviews of library staff working on collection management and EDID initiatives. A total of 44 publicly available library policy documents were analyzed, and 18 staff from university libraries across 6 Canadian provinces were recruited for semi-structured interviews. Key findings revealed several EDID initiatives which result in increased representation of equity-deserving groups in collections, removal of harmful and outdated terminologies in subject headings and library catalogues, and improvements in how materials are formatted by publishers and sourced by Canadian university libraries. My study also demonstrates the “double edge” roles of policy documents in sustaining and constraining the inclusion of equity-deserving groups in Canadian university libraries’ collections, evident in policy documents ensuring librarians’ agency and guiding collection decisions as well as lack of standardization and regular updates in these documents. Despite some success, participants reported challenges to collection management and EDID initiatives including funding constraints, difficulties in acquiring materials representing equity-deserving groups, limited staff diversity, bibliographic description challenges, accessibility issues, difficulty connecting with user communities, and inadequate institutional support for EDID initiatives. Drawing on participants’ shared experience and my analysis of policy documents, I propose recommendations such as intentional EDID actions, recruiting for diversity and representation, regular updates to policy documents, subject-based funding for collections, international acquisition strategies, regular diversity audits, and moving beyond minimum accessibility requirements, amongst others. The study ends with a call for further research on the impacts of Transformative Agreements and the National Indigenous Knowledge and Language Alliance (NIKLA) Respectful Terminologies Project on the collection management practices and EDID initiatives of Canadian university libraries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".