MétaCan
Menu
Back to cohort
Record W2093433911 · doi:10.1332/174426410x535846

Correlates of consulting research evidence among policy analysts in government ministries: a cross-sectional survey

2010· article· en· W2093433911 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEvidence & Policy · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversité du Québec à MontréalUniversité TÉLUQMcMaster UniversityÉcole Nationale d'Administration PubliqueUniversité Laval
FundersCanada Research Chairs
KeywordsPredictive powerCross-sectional studyRelevance (law)Government (linguistics)PsychologySurvey researchPower (physics)Political scienceApplied psychologyMedicine

Abstract

fetched live from OpenAlex

This large cross-sectional survey of policy analysts working in Quebec ministries (Canada) shows that direct interactions with academic researchers are among the most significant correlates of the consultation of scientific articles, academic research reports and academic books/chapters, but by very little compared to other correlates such as reported access to electronic bibliographic databases, training type, continuing professional development and perceived relevance of research evidence. Many correlates were found to have similar predictive power and, taken individually, all correlates have somewhat low predictive power. Interestingly, statistical simulations show that to achieve a larger predictive power, significant correlates must be manipulated simultaneously. Large variations were observed across policy sectors.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.044
metaresearch head score (Gemma)0.210
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0440.210
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.496
GPT teacher head0.626
Teacher spread0.130 · 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