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Record W2066945648 · doi:10.1002/ev.315

Knowledge translation: Implications for evaluation

2009· article· en· W2066945648 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.

Bibliographic record

VenueNew Directions for Evaluation · 2009
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsCanadian Institutes of Health Research
Fundersnot available
KeywordsKnowledge translationComputer scienceKnowledge managementField (mathematics)Translation (biology)Sustainability

Abstract

fetched live from OpenAlex

Abstract Translation theory originates in the field of applied linguistics and communication. The term knowledge translation has been adopted in health and other fields to refer to the exchange, synthesis, and application of knowledge. The logic model is a circular or iterative loop among various knowledge translation actors (knowledge producers and users) with translation activities evolving and occurring at various stages. Successful knowledge translation depends on the engagement of the target audience, as well as using the knowledge to inform decisions and have a positive influence on health outcomes. Understanding this alerts the evaluator to how to maximize the likely usefulness and sustainability of their evaluation research with local stakeholders. It also invites evaluators to help appreciate why programs have the short‐ and long‐term effects that they have, particularly any unintended or unexpected program outcomes that might have otherwise been puzzling. © Wiley Periodicals, Inc., and the American Evaluation Association.

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)0.015
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.537
GPT teacher head0.605
Teacher spread0.068 · 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