Knowledge Translation and Strategic Communications: Unpacking Differences and Similarities for Scholarly and Research Communications
Bibliographic record
Abstract
Knowledge translation (KT) involves communication of research evidence. Within research-relevant organizations there is considerable overlap in the roles and activities associated with KT and strategic communications (SC), which calls for greater role clarity. We untangle the differences and similarities between KT and SC, bringing clarity that may benefit organizations employing both types of workers. As KT practitioners (KTPs) take hold in organizations that have long had SC personnel, there is tension but also opportunities for defining roles and exploring synergies. What follows is a description of how we have explored this duality within our networks and an analysis of how SC and KT roles are similar and divergent.L’application des connaissances (AC) suppose la communication des données de la recherche. Dans les organisations qui s’occupent de recherche, les rôles et les activités associés à l’AC et aux communications stratégiques (CS) se recoupent en maints endroits, à tel point qu’une clarification des rôles s’impose. Nous démêlons ici les différences et les ressemblances entre l’AC et les CS, dans une mise a point utile aux organisations qui emploient les deux types de travailleurs. En effet, à mesure que les professionnels de l’AC prennent leurs marques dans des lieux de travail où s’affaire depuis longtemps un personnel voué aux communications, des tensions se créent, mais aussi des occasions de définir les rôles respectifs et de développer une synergie. Voici comment nous avons exploré cette dualité au sein de nos réseaux, ainsi qu’une analyse des ressemblances et des divergences entre les CS et l’AC.
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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 | 0.063 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.009 | 0.049 |
| Scholarly communication | 0.026 | 0.038 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".