Bibliographic record
Abstract
* Preface * Dilemmas in Balancing a University Literature Collection (David Isaacson) * Selection of Music Materials (Stephen Luttmann) * Selecting and Acquiring Art Materials in the Academic Library: Meeting the Needs of the Studio Artist (Elizabeth A. Lorenzen) * Native American Resources: A Model for Collection Development (Rhonda Harris Taylor and Lotsee Patterson) * Selecting and Acquiring Library Materials for Chinese Studies in Academic Libraries (Karen T. Wei) * Routes to Roots: Acquiring Genealogical and Local History Materials in a Large Canadian Public Library (Arthur G. W. McClelland) * Building a Dental Sciences Collection in a General Academic Library (Eva Stowers and Gillian Galbraith) * Nursing: Tools for the Selection of Library Resources (Janet W. Owens) * Acquisitions for Academic Medical and Health Sciences Librarians (Susan Suess) * Collection Development in Public Health: A Guide to Selection Tools (Lisa C. Wallis) * Selection in Exercise, Sport and Leisure (Mary Beth Allen) * Collection Development in a Maritime College Library (Jane Brodsky Fitzpatrick) * Collecting the Dismal Science: A Selective Guide to Economics Information Sources (Deborah Lee) * Collection Development Challenges for the 21st Century Academic Librarian (Susan Herzog) * Crossing Boundaries: Selecting for Research, Professional Development and Consumer Education in an Interdisciplinary Field, the Case of Mental Health (Patricia Pettijohn) * Retrospective Collection Development: Selecting a Core Collection for Research in New Thought (John T. Fenner and Audrey Fenner) * Stop the Technology, I Want to Get Off: Tips and Tricks for Media Selection and Acquisition (Mary S. Laskowski) * The Approval Plan: Selection Aid, Selection Substitute (Audrey Fenner) * Index * Reference Notes Included
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.372 | 0.267 |
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".