Bibliographic record
Abstract
I would like to take this opportunity to recognize and thank a number of people who have contributed so much to both the development of the Partnership network, as well as the journal, over the last two and half years. During the last year, three of the founding members of the Partnership have retired or stepped down. Larry Moore, Executive Director of Ontario Library Association has retired after a long and inspiring reign at OLA and will be greatly missed. Judith Silverthorne of the Saskatchewan Library Association stepped down in late 2007 and most recently Mike Burris, Executive Director of the British Columbia Library Association has accepted a new position with the Public Libraries in BC. On our editorial board, three of our founding editors, Heather Morrison (BCLA), Lorie Kloda (ABQLA) and Heather Berringer (APLA/OLA) will be finishing up their terms on the editorial board with the completion of this issue. I send out my heartfelt appreciation and admiration to all these wonderful, hardworking people for their contributions to the Canadian library community. It is their professionalism and enthusiasm, along with many others including Trudy Amirault, Chair of the Education Institute Committee that has fostered the development of this journal initiative as well as that of the Education Institute and the Career Centre.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.023 |
| Scholarly communication | 0.031 | 0.034 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.058 | 0.025 |
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".