Towards Place-Based Research to Support Social–Ecological Stewardship
Bibliographic record
Abstract
Concerns about ecological degradation and social inequalities have prompted increasing calls for stewardship in the social–ecological systems and sustainability science literature. However, how can the ideals of stewardship be realised in practice? The links between the theory and practice of stewardship are under-developed, and research to support place-based stewardship practice is limited. We therefore bring together complementary perspectives to guide research on place-based stewardship practice in the context of multifunctional landscapes. We unpack and synthesise literature on stewardship, landscapes, and collaboration for natural resource management, and highlight the ways in which the pathways approach can deepen research on collaboration and stewardship practice. We propose landscapes as a suitable level of analysis and action for stewardship. Since all landscapes are multifunctional, we argue that collaboration among multiple stakeholders is a necessary focus of such research. Our analysis reveals that existing theory on collaboration could be deepened by further research into the agency of individual human actors, the complex social–relational dynamics among actors, and the situatedness of actors within the social–ecological context. These factors mediate collaborative processes, and a better understanding of them is needed to support place-based stewardship practice. To this end, the pathways approach offers a waymark to advance research on collaboration, particularly in the complex, contested social–ecological systems that tend to characterize multifunctional landscapes.
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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 | 0.029 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".