A Best Practices Case Study for Scientific Collaboration between Researchers and Managers
Bibliographic record
Abstract
Effective engagement among scientists, government agency staff, and policymakers is necessary for solving fisheries challenges, but remains challenging for a variety of reasons. We present seven practices learned from a collaborative project focused on invasive species in the Great Lakes region (USA-CAN). These practices were based on a researcher-manager model composed of a research team, a management advisory board, and a bridging organization. We suggest this type of system functions well when (1) the management advisory board is provided compelling rationale for engagement; (2) the process uses key individuals as communicators; (3) the research team thoughtfully selects organizations and individuals involved; (4) the funding entity provides logistical support and allows for (5) a flexible structure that prioritizes management needs; (6) a bridging organization sustains communication between in-person meetings; and (7) the project team determines and enacts a project endpoint. We predict these approaches apply equally effectively to other challenges at the research-management-policy interface, including reductions of water pollution, transitions to renewable energy, increasing food security, and addressing climate change.
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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 | 0.064 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".