Designing and evaluating analytic-deliberative engagement processes for natural resources management
Bibliographic record
Abstract
The need to involve the public and stakeholders in decision-making around issues of technological complexity and conflicting values and knowledge systems is widely accepted in the field of natural resources management. Addressing both analysis and deliberation, analytic-deliberative processes are increasingly used for complex decision contexts. Yet, there remain significant disagreements about best practices for what constitutes a successful engagement process, be it analytic-deliberative, or otherwise. In response, theoretical frameworks and guidelines have been proposed to inform the design and evaluation of participatory engagement processes broadly. A common critique, however, is that the complexity and inflexibility of existing frameworks can make them inaccessible or impractical for natural resources managers and practitioners to use. Here, we propose a simple yet comprehensive framework for the design and evaluation of analytic-deliberative processes. We trial this framework in the context of an engagement process involving stakeholders and Indigenous peoples across the Canadian province of British Columbia on topics relating to forest carbon mitigation. Our recommendations highlight the importance of involving multiple actors, views and worldviews. We also emphasize the importance of inclusive deliberation that is based on the best available science, but also on other forms of expertise, including lay and traditional knowledge. Perhaps most importantly, our recommendations are consistent with others who call for opening-up analysis, deliberation and appraisal in participatory engagement. This means acknowledging that a one-size-fits-all solution does not always exist, but rather that plural and conditional policy options are often more advised in the context of complex environmental issues.
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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 | 0.374 | 0.482 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".