Systematic Reviews and the Evolving Role of Librarians
Bibliographic record
Abstract
The demand for systematic reviews (SR) in research intensive health related departments is rapidly increasing, and academic librarians have the expertise necessary to support these comprehensive reviews. In summer 2013, three University of Waterloo health librarians surveyed faculty on their current and future systematic review work. Through this process we determined researcher expectations of librarian support and identified multiple ways to meet their needs. Our poster illustrates possible librarian roles in the systematic review process, and how our expertise can be used towards knowledge creation.
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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 | 0.499 | 0.575 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.029 | 0.023 |
| Science and technology studies | 0.012 | 0.051 |
| Scholarly communication | 0.048 | 0.046 |
| Open science | 0.007 | 0.029 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".