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Record W2343804791 · doi:10.7892/boris.80905

Strengthening the food systems governance evidence base: Supporting commensurability of research through a systematic review of methods

2016· review· en· W2343804791 on OpenAlexaff
Aogán Delaney, Tom Evans, John Ryan McGreevy, Jordan Blekking, Tylor Schlachter, Peter A. Tamás, Todd Crane, Hallie Eakin, Wiebke Förch, Lindsey Jones, Donald R. Nelson, Christoph Oberlack, Mark Purdon

Bibliographic record

VenueBern Open Repository and Information System (University of Bern) · 2016
Typereview
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsCorporate governanceFood securityScholarshipAgency (philosophy)Commensurability (mathematics)Systematic reviewPolitical scienceBusinessSociologyGeographySocial scienceMEDLINE

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.618
metaresearch head score (Gemma)0.849
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.382
Threshold uncertainty score0.471

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6180.849
Meta-epidemiology (narrow)0.0040.005
Meta-epidemiology (broad)0.0150.011
Bibliometrics0.0590.043
Science and technology studies0.0040.013
Scholarly communication0.0240.027
Open science0.0110.017
Research integrity0.0130.009
Insufficient payload (model declined to judge)0.0110.002

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 head0.420
GPT teacher head0.540
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 designSystematic review
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

Citations5
Published2016
Admission routes1
Has abstractyes

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