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Record W3083387357 · doi:10.1016/j.envsci.2020.08.021

The CEEDER database of evidence reviews: An open-access evidence service for researchers and decision-makers

2020· article· en· W3083387357 on OpenAlexaff
Ko Konno, Samantha Cheng, Jacqualyn Eales, Geoff K Frampton, Christian Kohl, Barbara Livoreil, Biljana Macura, Bethan C. O’Leary, Nicola Randall, Jessica J. Taylor, Paul Woodcock, Andrew S. Pullin

Bibliographic record

VenueEnvironmental Science & Policy · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsCarleton University
FundersEconomic and Social Research Council
KeywordsIncentiveTransparency (behavior)Systematic reviewEvidence-based practiceReliability (semiconductor)Evidence-based policyResource (disambiguation)Service (business)Grey literatureComputer scienceKnowledge managementBusinessManagement sciencePolitical scienceMEDLINEMarketingEconomicsMedicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.041
metaresearch head score (Gemma)0.314
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.456

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.314
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0210.005
Bibliometrics0.1080.104
Science and technology studies0.0020.002
Scholarly communication0.0210.010
Open science0.0080.012
Research integrity0.0100.005
Insufficient payload (model declined to judge)0.1360.036

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 head0.787
GPT teacher head0.654
Teacher spread0.133 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreMethods

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

Citations25
Published2020
Admission routes1
Has abstractno

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