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Record W2613384262 · doi:10.1177/1044207317694840

Deliberative Dialogues Between Policy Makers and Researchers in Canada and Australia

2017· article· en· W2613384262 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Disability Policy Studies · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreHospital for Sick ChildrenUniversity of Toronto
FundersNational Health and Medical Research Council
KeywordsCoproductionKnowledge translationPublic relationsPolitical scienceScience policyField (mathematics)Health careKey (lock)Knowledge managementEngineering ethicsPublic administrationComputer scienceEngineering

Abstract

fetched live from OpenAlex

Knowledge translation (KT) and implementation science are growing fields in Canada, Australia, and worldwide. Many audiences are targeted as KT knowledge users—policy makers represent one key knowledge user in the health care field. The need for policy makers to understand research and for researchers to understand policy processes is commonly recognized. There is also increasing interest in health policy that focuses on KT as a framework for understanding the use of evidence and, in particular, describing the influence of research on policy along with concepts of coproduction and user involvement. With relationship building central to successful evidence-informed policy, this article explores deliberative dialogue as a potential approach to enhancing KT. It describes two examples of researcher efforts to cultivate relationships and contacts with policy and decision makers via such dialogues and illustrates the inherent opportunities and challenges of doing so.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.726
GPT teacher head0.591
Teacher spread0.134 · 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