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Record W2094453063 · doi:10.1007/s00003-014-0897-5

Science into policy; improving uptake and adoption of research: outcomes and conclusions

2014· article· de· W2094453063 on OpenAlexaff
Paul De Barro, S.L. Goldson, Detlef K. Bartsch, M. Hirsch, Martyn Jeggo, John W. Lowenthal, Philip Macdonald, Ryan R. J. McAllister, Rod McCrea, Fiona McFarlane, Cathy Robinson, Kim Ritman, Joe Smith, Rieks D. van Klinken, Iain Walker, Juliana Ribeiro Alexandre, Simon C. Barry, Camilla Beech, Craig Cormick, Peter Kearns, Qu Liang, Aditi Mankad, Sally L. McCammon, Sylvie Mestdagh, Sorada Tapsuwan, Andrea Walton

Bibliographic record

VenueJournal of Consumer Protection and Food Safety · 2014
Typearticle
Languagede
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsCanadian Nuclear Safety CommissionCanadian Food Inspection Agency
Fundersnot available
KeywordsScience policyPsychologyPolitical sciencePublic administration

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)0.269
metaresearch head score (Gemma)0.451
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.902

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2690.451
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0060.014
Science and technology studies0.0030.011
Scholarly communication0.0210.018
Open science0.0050.010
Research integrity0.0170.009
Insufficient payload (model declined to judge)0.0170.002

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 head0.169
GPT teacher head0.468
Teacher spread0.299 · 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
DomainEvaluation
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

Citations2
Published2014
Admission routes1
Has abstractno

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