Bibliographic record
Abstract
Welcome to the August 2024 issue of JCHLA/JABSC!I hope everyone is enjoying the summer weather and taking some time to rest and relax.I am very pleased to present this issue which contains a special section on literature related to health librarianship and libraries during the COVID-19 pandemic.This was an extraordinary time to work in any health-related field, and health libraries were no exception.We have several program descriptions that showcase the innovative work undertaken by health librarians during this time.Additionally, we have a column from the librarians and library technicians of Saskatchewan Health Authority Library who won the CHLA/ABSC Flower Award for Innovation in 2022 for their work during the COVID-19 pandemic.This issue also includes two book reviews on post-pandemic libraries and accreditation in the health sciences and a product review of the Scopus search analyzer.I was fortunate to be able to attend the wonderful CHLA/ABSC conference in beautiful Winnipeg in June.It was great to see colleagues from across the country come together and talk about all things health librarianshipwe are certainly a small but mighty community.Abstracts for the conference's contributed papers, lightning talks, and posters can be found in this issue.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.025 | 0.027 |
| Insufficient payload (model declined to judge) | 0.054 | 0.041 |
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".