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Record W2782500369 · doi:10.22230/src.2017v8n1a274

Integrated Knowledge Transfer and Exchange: An Organizational Approach for Stakeholder Engagement and Communications

2017· article· en· W2782500369 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueScholarly and Research Communication · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of TorontoInstitute for Work & Health
Fundersnot available
KeywordsKnowledge transferKnowledge managementStakeholderComputer scienceProcess (computing)Information exchangeKey (lock)Organizational learningVariety (cybernetics)Process managementBusinessPublic relationsTelecommunicationsPolitical science

Abstract

fetched live from OpenAlex

Knowledge transfer and exchange (KTE) is a process of making relevant research information available and accessible for use in practice or policy. Integrated KTE, where knowledge users are engaged in the research process, is considered to better facilitate uptake and use. The objective of this paper is to describe a fully integrated KTE approach developed over the past 20 years. Key concepts related to knowledge user engagement as well as the integration of communications within KTE are described. The organizational KTE approach is flexible and can be adapted to a variety of research areas.

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)0.018
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0140.001
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.884
GPT teacher head0.688
Teacher spread0.196 · 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