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Record W7164358749 · doi:10.1184/r1/32632413

Conduct and reporting of search methods: An assessment of Campbell Collaboration systematic reviews

2023· other· W7164358749 on OpenAlexaboutno aff
Sarah Young, Alison Bethel, Ursula Ellis, Diana Louden, Heather MacDonald, David Pickup, Zahra Premji, Morwenna Rogers

Bibliographic record

VenueKiltHub Repository · 2023
Typeother
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSystematic reviewTransparency (behavior)Grey literatureQuality (philosophy)Best practicePeer review

Abstract

fetched live from OpenAlex

Searching the literature is a critical step in generating high quality and comprehensive systematic reviews. Campbell reviews are required to adhere to standards for conducting and reporting on searches, but it is unclear how well published reviews meet standards and provide for transparency and reproducibility of the search methods. Better understanding current practices in searching in Campbell reviews can inform the development of training, guidance, and improved peer review processesPresented at the 2023 What Works Global Summit, Ottawa, Canada

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.881
metaresearch head score (Gemma)0.955
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.119
Threshold uncertainty score0.431

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.8810.955
Meta-epidemiology (narrow)0.0050.009
Meta-epidemiology (broad)0.0150.020
Bibliometrics0.0490.062
Science and technology studies0.0120.017
Scholarly communication0.0280.021
Open science0.0140.026
Research integrity0.0120.014
Insufficient payload (model declined to judge)0.0090.004

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.

Opus teacher head0.229
GPT teacher head0.532
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainReporting
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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