Bibliographic record
Abstract
The UK Higher Education Academy has commissioned and made available four online modules for librarians interested in undertaking systematic reviews. The Units are aimed at undergraduates in library and information studies preparing for their final project or dissertation. Postgraduates in library and information science should also find the materials relevant to their research training. Using examples from the library and information science literature the modules take the user through topics needed to carry out a systematic review including: what is a systematic review, formulating searches for research evidence, producing a systematic review (sifting and grading evidence) and meta-analysis, meta-synthesis and guidelines. The Units complement, but do not replace existing research methods modules. Authored by Dr. Christine Urquhart, Alison Yeoman and Dina Tbaishat from the University of Aberystwyth, UK, and Alison Brettle from the University of Salford, UK, the modules are available online or to download from http://www.ics.heacademy.ac.uk/resources/rlos/systematic_review/.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.511 | 0.272 |
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".