Bibliographic record
Abstract
This is my farewell issue, my final issue, as Editor of School Libraries Worldwide. I begin by welcoming most enthusiastically the two new Editors, Dr. Marcia Mardis and Dr. Nancy Everhart, both faculty members at the Florida State University's College of Information. Both Dr. Mardis and Dr. Everhart are familiar to SLW readers, and, coincidentally, each had an article published in the last hard copy issue of the journal, four years ago, in July 2006. I am confident that, under their leadership, School Libraries Worldwide will go places that I could never have taken the journal, and I look forward to seeing their new directions unfold for the journal and its readers.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.121 | 0.056 |
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".