Exploring Alternative Formats of University-Community Engaged Knowledge Mobilization
Bibliographic record
Abstract
This paper discusses growing interest in using alternative formats (e.g., videos, clear language summaries, infographics) in scholarly communication and knowledge mobilization. We draw upon two distinct examples: 1) a community-engaged learning pilot project (2019-2023) and 2) a multiyear research project (2018-2023) that were connected to a larger university-community initiate to make research accessible to non-university audiences. We focus on formal and informal assessments of alternative formats within these projects for various constituents, e.g., students, instructors, community participants. We conclude that more work is needed to understand the value and utility of alternative formats, especially for community knowledge users. Explorer des formats alternatifs de mobilisation des connaissances entre l'université et la communauté RésuméCet article traite de l'utilisation de formats alternatifs (par exemple, vidéo, résumé en langage clair, infographie) dans la communication savante et dans la mobilisation des connaissances. Nous nous appuyons sur deux exemples : 1) un projet d'apprentissage par l’engagement communautaire (2019-présent) et 2) un projet de recherche pluriannuel (2018-2023), tous deux liés à une plus grande initiative de la communauté universitaire visant à rendre la recherche accessible à des publics non universitaires. Nous nous concentrons sur l'utilisation de formats alternatifs dans le cadre de ces projets et sur les efforts visant à évaluer leur valeur pour les différentes parties prenantes, par exemple les étudiants, les instructeurs, les chercheurs et les participants de la communauté. Nous rendons compte de ce que nous avons appris à travers ces projets et soutenons qu'il est essentiel de comprendre l'utilité des formats alternatifs afin de motiver les chercheurs à mobiliser leur travail et pour soutenir l'utilisation de la recherche parmi les utilisateurs de la communauté. Mots-clésrecherche communautaire; communication savante; évaluation de la recherche; formats alternatifs
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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 | 0.030 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".