Why do researchers co‐author evidence syntheses with librarians? A mixed‐methods study
Bibliographic record
Abstract
Librarians and information specialists are experts in designing comprehensive literature searches, such as those needed for Evidence Syntheses (ES). The contributions of these professionals to ES research teams have several documented benefits, especially when they collaborate on the project. However, librarian co-authorship is relatively rare. This study explores researcher motivations for working with librarians at the co-author level through a mixed methods design. Interviews with researchers identified 20 potential motivations that were then tested through an online questionnaire sent to authors of recently published ES. Consistent with previous findings, most respondents did not have a librarian co-author on their ES, though 16% acknowledged one in their manuscript and 10% consulted one but did not document the contribution. Search expertise was the most common motivation both to and not to co-author with librarians. Those that had or were interested in co-authoring stated that they wanted the librarians' search expertise, while those who had not or were not interested stated that they already had the necessary search expertise. Researchers who were motivated by methodological expertise and availability were more likely to have co-authored their ES with a librarian. No motivations were negatively associated with librarian co-authorship. These findings provide an overview of the motivations that influence researchers to bring a librarian into an ES investigatory team. More research is needed to substantiate the validity of these motivations.
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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 | 0.428 | 0.689 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".