Embracing Implementation Science to Enhance Fisheries and Aquatic Management and Conservation
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 The management and conservation of fisheries and aquatic resources are inherently applied activities. Therefore, when knowledge generated from research and monitoring, or knowledge that is held by practitioners and other actors (e.g., Indigenous elders, fishers), fails to inform those applied decisions, the persistent gap between knowledge and action is reinforced (i.e., the knowledge–action gap). In the healthcare realm, there has been immense growth in implementation science over the past decade or so with a goal of understanding and bridging the gap between knowledge and action and delivering on evidence-based decision making. Yet, within fisheries and aquatic sciences, the concept of implementation science has not received the same level of attention. We posit, therefore, that there is an urgent need to embrace implementation science to enhance fisheries and aquatic management and conservation. In this paper, we seek to describe what implementation science is and what it has to offer to the fisheries and aquatic science and management communities. For our context, we define implementation science as the scientific study of processes and approaches to promote the systematic uptake of research and monitoring findings and other evidence-based practices into routine practice and decision making to improve the effectiveness of fisheries management and aquatic conservation. We explore various frameworks for implementation science and consider them in the context of fisheries and aquatic science. Although there are barriers and challenges to putting implementation science into practice (e.g., lack of capacity for such work, lack of time to engage in reflection, lack of funding), there is also much in the way of opportunity and several examples of where such efforts are already underway. We conclude by highlighting the research needs related to implementation science in the fisheries and aquatic science realm that span methodological approaches, albeit a common theme is the need to involve practitioners (and other relevant actors) in the research. By introducing the concept and discipline of implementation science to the fisheries and aquatic science community, our hope is that we will inspire individuals and organizations to learn more about how implementation science can help deliver on the promise of evidence-based management and decision making and narrow the gap between research and practice.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
| grok | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
| opus | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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