Facing the challenges of using place-based social-ecological research to support ecosystem service governance at multiple scales
Bibliographic record
Abstract
Place-based social-ecological research is often designed to improve local environmental governance, but it can also inform decisions at larger scales or in other places. However, the focus on local perspectives in such research creates challenges for transferring insights to other locations, and for aggregating understanding to larger scales. In this paper, we discuss how ResNet, a new pan-Canadian network of researchers working on place-based social-ecological case studies via ecosystem services, will face (and hopefully overcome) these challenges while taking advantage of the unique benefits of a place-based approach. Drawing on insights from the literature and from the first 10 years of the Programme for Ecosystem Change and Society (PECS), we outline solutions to six key challenges to multi-scale knowledge integration across place-based cases, and explore how ResNet is employing some of these solutions.
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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 | 0.055 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.005 | 0.006 |
| 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".