Bibliographic record
Abstract
<JATS1:p>Begins where diversity audits end, informing and supporting academic, school, and public librarians in the quest to embed diversity, equity, and inclusion in a meaningful and sustainable manner throughout collections, policies, and practices.</JATS1:p> <JATS1:p>A primary question for many librarians, directors, and board members is how to evaluate diversity in a collection on an ongoing basis.</JATS1:p> <JATS1:p>Curating Community Collections provides librarians with the tools they need to understand the results of diversity audits and to formulate a reasonable, achievable plan for increasing diversity, equity, and inclusion not only in the collection itself, but also in library collection policies and practices. Information on ways to make diversity, equity, and inclusion part of a library's everyday workflow will help ensure the sustainability of these principles.</JATS1:p> <JATS1:p>Mary Schreiber and Wendy Bartlett teach readers how to increase the number of diverse materials in their collections and make them more discoverable to library patrons through the implementation of a community collections program. Stories from librarians around the United States and Canada who are auditing and improving the diversity of their collections add broad, scalable perspectives for libraries of any size, budget, and mission. Action steps provided at the end of each section offer a practical road map for all types of libraries to curate a diverse, equitable, and inclusive community collection.</JATS1:p>
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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 | 0.029 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.026 | 0.012 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.006 | 0.045 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.043 | 0.021 |
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".