Bibliographic record
Abstract
Evidence Synthesis Institute (ESI) Canada hosted its annual event virtually from March 18-21, 2024, offering an in-depth training opportunity on evidence synthesis (ES) methods. This report outlines and contextualizes the goals and objectives of the workshop, with reflections from the perspective of an attendee. The intensive four-day workshop, conducted in partnership between the Canadian Association of Research Libraries (CARL) and the University of Victoria Libraries, aimed to enhance librarians’ capabilities in supporting systematic reviews and other ES projects across various academic disciplines. Originally inspired by a U.S. model and first piloted in 2022, the event focused on equipping participants with the foundational knowledge needed to apply rigorous, systematic, transparent, and reproducible techniques for literature synthesis crucial for producing high-quality research outputs with reduced biases. The curriculum included topics covering the full lifecycle of an ES review, and sessions were designed to provide both foundational ES knowledge and practical application strategies to integrate the training into participants’ professional roles. The workshop facilitated a collaborative and supportive environment, enabling participants to network with peers and experts, fostering a national community of practice. By preparing librarians to undertake more substantive roles in ES research projects, including as co-investigators, ESI Canada significantly contributes to skillset development, addressing increasing demand in this area. This workshop is particularly invaluable for early career librarians and those involved in interdisciplinary research support, furthering a precedent for effective ES training that is likely to influence future academic library services.
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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 | 0.057 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.022 | 0.004 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.207 | 0.078 |
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".