Benchmarking Librarian Support of Systematic Reviews in the Sciences, Humanities, and Social Sciences
Bibliographic record
Abstract
Systematic reviews, along with other types of knowledge synthesis, are a research methodology that attempt to find all available evidence on a topic to help answer specific questions. Librarian involvement in systematic reviews is well established in the health sciences and in recent years there has been growing awareness of, and literature about, librarians outside of health supporting systematic reviews. This study benchmarks librarian support of systematic reviews in the sciences, humanities, and social sciences (SHSS) by looking at the growth of demand for support, the disciplines requesting this kind of librarian support, and the specific types of support needed. It also delves into what SHSS librarians need to be successful in this type of work, including administrative support and workload adjustments.
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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 | 0.558 | 0.881 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.035 | 0.061 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".