A collaborative approach to bridging the gap between wildlife managers and researchers
Bibliographic record
Abstract
ABSTRACT Although most wildlife professionals agree that science should inform wildlife management decisions, disconnect still exists between researchers and managers. If researchers are not striving to incorporate their findings into management decisions, support for research programs by managers can wane. If managers are not using research findings to inform management decisions, those decisions may be less effective or more vulnerable to legal challenges. Both of these situations can have negative consequences for wildlife conservation. We outline a collaborative research‐management approach to bridging the gap between wildlife managers and researchers. We describe differences in perspectives, perceptions, and priorities between managers and researchers; outline how and why the divide between researchers and managers has likely occurred and continues to grow; and present specific strategies and recommendations to foster stronger collaborations between managers and researchers. We advocate increased synergy between managers and researchers based on a shared vision of conservation and a collaborative structure that rewards researchers and managers. Most importantly, we suggest that relationships and communication between managers and researchers must be established early in research development and decision‐making processes, fostering the trust needed for collaboration. Institutions and agencies can facilitate these relationships by creating opportunities and incentives for integrating collaborative research into management decisions. We suggest this approach will strengthen ties between researchers and managers, increase relevance of research to management decisions, promote effectiveness of management decisions, reduce legal challenges, and ultimately produce positive, tangible, and lasting effects on wildlife conservation. © 2019 The Wildlife Society.
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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 | 0.229 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.031 | 0.027 |
| Scholarly communication | 0.032 | 0.027 |
| Open science | 0.009 | 0.069 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 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".