Librarian involvement on knowledge synthesis articles and its relationship to citation counts and Journal Impact Factor
Bibliographic record
Abstract
Introduction: Our aim was to determine if there is a relationship between librarian involvement on a knowledge synthesis project and the synthesis's citation count or the Journal Impact Factor (JIF) of its publication venue. Methods: A total of 551 knowledge syntheses published during a one-year period (2020) from a single category, "Psychology, Clinical", in Clarivate's Journal Citation Reports were exported from Web of Science along with the citation counts for each synthesis and the JIF of its publication venue. The full-text of each article was examined in order to code each as either co-author, acknowledged, or unknown to reflect the level of librarian involvement in the synthesis. The Wilcoxon Rank Sum test on bootstrapped samples was used to determine the significance of the results. Results: Librarians were co-authors or acknowledged in 80 (15%) of the syntheses examined. Analyzing two levels of librarian involvement (involved, unknown) indicated no relationship between the level of librarian involvement and the JIF of the journal nor the citation count the synthesis received since publication. Discussion: There is no evidence of a relationship between librarian involvement in knowledge syntheses and the JIF of the publication or citation count of documents published in journals falling in the JCR category of "Psychology, Clinical" in the year 2020. Repeating this methodology in a different JCR category could help determine whether this lack of a relationship extends beyond the "Psychology, Clinical" category.
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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 | 0.120 | 0.552 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.037 | 0.056 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".