Co-production and conservation physiology: outcomes, challenges and opportunities arising from reflections on diverse co-produced projects
Bibliographic record
Abstract
As a relatively nascent discipline, conservation physiology has struggled to deliver science that is relevant to decision-makers or directly useful to practitioners. A growing body of literature has revealed that co-produced research is more likely to generate knowledge that is not only relevant, but that is also embraced and actionable. Co-production broadly involves conducting research collaboratively, inclusively, and in a respectful and engaged manner-spanning all stages from identifying research needs to study design, data collection, interpretation and application. This approach aims to create actionable science and deliver meaningful benefits to all partners involved. Knowledge can be co-produced with practitioners/managers working for regulators or stewardship bodies, Indigenous communities and governments, industry (e.g. fishers, foresters, farmers) and other relevant actors. Using diverse case studies spanning issues, taxa and regions from around the globe, we explore examples of co-produced research related to conservation physiology. In doing so, we highlight benefits and challenges while also identifying lessons for others considering such an approach. Although co-production cannot guarantee the ultimate success of a project, for applied research (such as what conservation physiology purports to deliver), embracing co-production is increasingly regarded as the single-most important approach for generating actionable science to inform conservation. In that sense, the conservation physiology community would be more impactful and relevant if it became commonplace to embrace co-production as demonstrated by the case studies presented here.
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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 | 0.068 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.028 | 0.050 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.006 | 0.041 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".