Open Scholarly Publishing and Knowledge Mobilization: Combining Two Initiatives to Achieve Social Impact
Bibliographic record
Abstract
Trends within universities and scholarly publishers are converging to develop tools and services to maximize the societal impacts of research. The open research agenda underpins drives to improve accessibility to knowledge, while the academic community is increasingly tasked to generate “impact” on society. With obvious synergies between these agendas, it is increasingly important for collaboration across the research ecosystem to build on this complementarity. This article reflects on, and conjects a future for, academic-publisher collaborations to connect these agendas. Les tendances au sein des universités et des éditeurs savants convergent pour développer des outils et des services permettant de maximiser les impacts sociétaux de la recherche. L’agenda de la recherche de libre accès sous-tend les efforts visant à améliorer l’accessibilité aux connaissances, tandis que la communauté universitaire est de plus en plus chargée de générer un « impact » sur la société. Compte tenu des synergies évidentes entre ces agendas, il est de plus en plus important que la collaboration au sein de l’écosystème de la recherche s’appuie sur cette complémentarité. Cet article est le fruit d’une réflexion et d’une conjecture sur l’avenir des collaborations entre les universités et les éditeurs afin de relier ces agendas. Il est rédigé conjointement par un gestionnaire de l’impact de la recherche et un chercheur universitaire (Bayley), un praticien de la mobilisation des connaissances universitaire (Phipps) et des collègues d’Emerald Publishing (Roche et Lodge).
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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 | 0.073 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.016 | 0.063 |
| Scholarly communication | 0.072 | 0.052 |
| Open science | 0.005 | 0.085 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".