Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it