Bibliographic record
Abstract
With this issue, after seven years of production, the format and focus of School Libraries Worldwide has changed. The Editorial Board of School Libraries Worldwide, at its meeting in July 2001 at the IASL conference in Auckland, New Zealand, decided that a shift in the balance of SLW content was timely. This shift in the content emphasis of the journal, toward more research dissemination, will take School Libraries Worldwide one step closer to being the preeminent research journal in our field. This goal has come into the range of the possible because of the support of the Association for the journal and because of the success of another IASL initiative, the International Forum on Research in School Librarianship. These initiatives, along with the Association's research award program, reflect the increasing interest in and support of research within the Association.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.008 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.035 | 0.036 |
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".