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Conceptual recommendations for selecting the most appropriate knowledge synthesis method to answer research questions related to complex evidence

2016· review· en· W2275535763 on OpenAlexafffund
Monika Kastner, Jesmin Antony, Charlene Soobiah, Sharon E. Straus, Andrea C. Tricco

Bibliographic record

VenueJournal of Clinical Epidemiology · 2016
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsCanada Research ChairsSt. Michael's HospitalUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMultidisciplinary approachComputer scienceField (mathematics)Knowledge baseData scienceProcess (computing)Knowledge managementManagement scienceArtificial intelligenceMathematicsSociology

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)0.447
metaresearch head score (Gemma)0.661
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.553
Threshold uncertainty score0.682

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4470.661
Meta-epidemiology (narrow)0.0040.005
Meta-epidemiology (broad)0.0100.016
Bibliometrics0.0270.020
Science and technology studies0.0060.011
Scholarly communication0.0210.023
Open science0.0110.013
Research integrity0.0120.019
Insufficient payload (model declined to judge)0.0250.007

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 head0.980
GPT teacher head0.860
Teacher spread0.120 · 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 designTheoretical or conceptual
DomainMethods
GenreReview

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

Citations104
Published2016
Admission routes2
Has abstractno

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