CHLA 2023 Conference Lightning Talks/Congrès de l'ABSC 2023: Présentations Éclair
Bibliographic record
Abstract
Introduction: There are numerous benefits for researchers to involve a librarian in their knowledge synthesis projects such as better search precision and higher quality search strategy and methodological reporting. However, does librarian involvement make a difference when it comes to publication venue? This research examines if any correlation exists between librarian involvement on published knowledge syntheses, and the impact factor of the journals where they are published. Methods: Focusing on the journals from a single category ('Psychology, Clinical') in Clarivate's Journal Citation Reports (JCR), the authors analyzed the librarian involvement (co-author, acknowledged, unclear or none) in a complete set of English language knowledge syntheses published over a one-year period (2020). Results: Librarians were co-authors on 2.7%, and acknowledged in 11.7% of the 551 knowledge syntheses examined. Dividing the included documents into similar sized groups, we defined four impact factor ranges: G1=12.792 to 7.169; G2=6.724 to 4.507; G3=4.473 to 3.368; G4=3.311 to 0.342. The proportion of knowledge syntheses with librarian co-authors and librarian acknowledgements were, respectively; G1: 3% and 11.1%; G2: 2.1% and 7.7%; G3: 5.1% and 16.7%; and G4: 0.7% and 11.9%. Discussion: Although G3 showed higher percentages of librarian co-authorship and acknowledgment than the other groups, the number of librarian co-authored papers in the document set was too low for chi-squared significance testing. Further research in a JCR category with a higher proportion of librarian co-
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.573 | 0.301 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".