Inclusion of librarians and information professionals in Canadian knowledge synthesis grant funding
Bibliographic record
Abstract
Introduction: Librarians continually advocate for the expertise they can bring to knowledge synthesis research projects. Professional associations like the Canadian Health Libraries Association aim to promote librarians and information professionals as partners in health research. This push for representation must happen at a policy level to enact change. To that purpose, we explored the degree to which the inclusion of librarians and information professionals is represented at the funding level by healthcare research organizations in Canada. Methods: We used a list of health research funding agencies generated from Scopus searches and an independent search of Canadian health research institutions, governmental health authorities, professional associations, and research-oriented universities to identify research grants designed for knowledge synthesis research. We examined these grants to determine whether they require or specifically mention librarians in their eligibility criteria. Results: Of the 14 knowledge synthesis grants we identified, only one required a health librarian as a member of the research team in the grant eligibility criteria. Four grants “strongly recommended” the inclusion of librarians on the research team, though this inclusion was not a contingency for funding. Discussion: Most knowledge synthesis grants in Canada do not require, recommend, or mention librarians as members of the research or authorship team. Evidence suggests that librarian involvement substantially improves the quality of knowledge synthesis research projects; it would therefore benefit both librarians and knowledge synthesis work to advocate for librarian involvement as a contingency for grant funding.
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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 | 0.276 | 0.527 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.030 | 0.046 |
| Science and technology studies | 0.031 | 0.016 |
| Scholarly communication | 0.025 | 0.011 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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; 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".