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Record W4383313579 · doi:10.1080/00336297.2023.2209331

Partnering for Impact: A Blueprint for Knowledge Translation Initiatives in the Canadian Sport Sector

2023· article· en· W4383313579 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

VenueQuest · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsBrock UniversityToronto Rehabilitation InstituteUniversity of TorontoYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsBlueprintKnowledge translationPublic relationsBusinessPolitical scienceKnowledge managementEngineeringComputer science

Abstract

fetched live from OpenAlex

Evaluation is an essential organizational practice in sport, but many organizations do not have adequate capacity to engage in evaluative work. To address this gap, academic researchers partnered with Canada’s Sport Information Resource Centre, a nationally serving nonprofit dedicated to knowledge translation in sport, to develop, deliver and evaluate a series of webinars and knowledge products (e.g. blog posts, videos) that aimed to build evaluation capacity among sport organizations. The initiative produced four webinars and 16 knowledge products that reached 753 sport stakeholders, with 86% of survey respondents reporting an increase in evaluation knowledge. Using the Knowledge to Action and RE-AIM frameworks, this paper provides a blueprint for higher education professionals seeking to co-develop, co-deliver and co-evaluate knowledge translation initiatives in partnership with nonprofit sport organizations in Canada.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.833
GPT teacher head0.717
Teacher spread0.116 · 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