Bibliographic record
Abstract
Academic library workers often make use of systemic, bureaucratic, political, collegial, and symbolic dimensions of organizational behavior to achieve their diversity, equity, and inclusion goals, but many are also doing the crucial work of pushing back at the structures surrounding them in ways small and large. Implementing Excellence in Diversity, Equity, and Inclusion captures emerging practices that academic libraries and librarians can use to create more equitable and representative institutions. 19 chapters are divided into 6 sections:Recruitment, Retention and PromotionProfessional DevelopmentLeveraging Collegial NetworksReinforcing the MessageOrganizational ChangeAssessmentChapters cover topics including active diversity recruitment strategies; inclusive hiring; gendered ageism; librarians with disabilities; diversity and inclusion with student workers; residencies and retention; creating and implementing a diversity strategic plan; cultural competency training; libraries’ responses to Canadian Truth and Reconciliation Commission Calls to Action; and accountability and assessment. Authors provide practical guiding principles, effective practices, and sample programs and training. Implementing Excellence in Diversity, Equity, and Inclusion explores how academic libraries have leveraged and deployed their institutions’ resources to effect DEI improvements while working toward implementing systemic solutions. It provides means and inspiration for continuing to try to hire, retain, and promote the change we want to see in the world regardless of existing structures and systems, and ways to improve those structures and systems for the future.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.614 | 0.545 |
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".