Information retrieval and the role of the information specialist in producing high-quality systematic reviews in the social, behavioural and education sciences
Bibliographic record
Abstract
English The International Campbell Collaboration (C2) prepares, maintains and disseminates high-quality systematic reviews in the social, behavioural and educational sciences. As part of its effort to ensure that systematic reviews are based on a set of systematic, transparent and replicable procedures, C2 has produced a set of policy briefs. One of these, the C2 Information retrieval policy brief, proposes policies for searching the literature for C2 reviews, addresses key issues and challenges faced by C2 reviewers, and recommends working with an information specialist (IS). This article illustrates how the information retrieval issues raised in the brief have been addressed by one C2 review team, through the inclusion of an IS as an integral member of its review team. This unique approach recognises that information retrieval is a continuous and important process, requiring the ongoing expertise of a professional. Cost implications for the provision of ongoing support by an IS are briefly addressed, along with various alternative approaches.
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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 | 0.805 | 0.917 |
| Meta-epidemiology (narrow) | 0.004 | 0.008 |
| Meta-epidemiology (broad) | 0.016 | 0.006 |
| Bibliometrics | 0.058 | 0.049 |
| Science and technology studies | 0.016 | 0.049 |
| Scholarly communication | 0.063 | 0.081 |
| Open science | 0.010 | 0.037 |
| Research integrity | 0.033 | 0.026 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".