On “success” in applied environmental research — What is it, how can it be achieved, and how does one know when it has been achieved?
Bibliographic record
Abstract
Environmental decision-makers and practitioners need and deserve high-quality environmental evidence for effective decision-making. We collate and share a suite of best practices for applied environmental researchers to support their capacity to inform such decision-making processes. This raises a number of important questions: What does “relevant” and informative evidence look like? How do we know when evidence has been applied? We assembled an experienced team of knowledge generators and users in Canada to identify insights that have emerged from their work and that could serve as guideposts for others who seek to apply environmental research to policy challenges. By reflecting on successes and failures, we define “success” in applied environmental science as respectfully conducted, partner-relevant research that is accessible, understandable, and shared and that can create opportunities for change (e.g., in policy, behaviour, management). Next, we generated a list of best practices for delivering “successful” applied environmental research. Our guidance emphasizes the importance of engaging early and often, in a respectful manner, with partners, generating high-quality, relevant research (which requires flexibility), having a plan for communicating and sharing outputs, and being transparent about uncertainties and limitations. Other important considerations include acknowledging partners for involvement and training early career researchers in applied partnership research. Finally, we generated a list of specific, measurable indicators for evaluating success, including quality and quantity of scientific outputs, the relationship with the partner(s), relevance and connectedness of the research, accessibility and availability of outputs to users, provision of outputs that are digestible and usable by different audiences, training and capacity building, and ultimate outcomes (e.g., including social, environmental, and economic outcomes, as well as partner satisfaction). We encourage those embarking on applied environmental research to consider embracing the strategies, to continuously reflect on progress toward shared research goals, and to be flexible. Doing so will increase the likelihood of delivering research that is “successful” and in doing so contribute to overcoming and addressing environmental issues and problems.
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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 | 0.411 | 0.494 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.021 | 0.066 |
| Scholarly communication | 0.058 | 0.041 |
| Open science | 0.006 | 0.036 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".