Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.348 | 0.663 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.027 | 0.038 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".