Insights from a novel, user-driven science transfer program for resource management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it