Insights from a novel, user-driven science transfer program for resource management
Bibliographic record
Abstract
Abstract Research results are often not easily accessible or readily digestible for decision-making by natural resource managers. This knowledge-action gap is due to various factors including the time lag between new knowledge generation and its transfer, lack of formal management structures, and institutional inertia to its uptake. Herein, we reflect on the Great Lakes Fishery Commission’s Science Transfer Program and its evolution from ‘Mode 1’ (i.e., scientists conduct research autonomously) toward ‘Mode 2’ (i.e., co-production of knowledge with practitioners) knowledge production to understand and overcome the knowledge-action gap. Six success factors and strategies and tactics used to achieve those factors were critical to the shift from Mode 1 to Mode 2: (1) dedicate funding and staff support; (2) obtain top-down commitment from organizational leadership; (3) break down silos; (4) build relationships through formal and informal interactions; (5) emphasize co-production in program and project implementation; and (6) obtain buy-in among relevant actors. By way of three project case studies, we highlight knowledge transfer approaches, products, and lessons learned. We anticipate this contribution will benefit those working on knowledge mobilization, particularly in boundary-spanning organizations, and those involved in resource program management, administration, and design; it is also intended for resource managers seeking to have their science and information needs met more effectively.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".