Fostering care and agency for wildlife stewardship on Indigenous and local lands: the power of place, practice, and virtue
Bibliographic record
Abstract
Stewardship of wildlife within lands managed by Indigenous peoples and local communities has relied on traditional knowledge and skills yet faces challenges amid social and economic changes that can weaken enduring social-ecological relations. Wildlife conservation initiatives frequently lean on community-based wildlife management (CBWM) to foster sustainable use while ensuring community involvement in decision making. In practice, CBWM has often remained tightly linked to top-down or externally driven processes with quick negative conclusions being drawn about the capacity of communities to manage local wildlife, without further investigating the foundational conditions of stewardship. This study proposes a theoretical model for place-based stewardship that builds on the care-knowledge-agency framework, grounding it in mentored practices and environmental virtue. Our model does not discount the importance of devolved governance systems and legitimate leadership but highlights the parallel requirement for virtuous local decision-making processes, which emerge from mentored practice, rooted in place-based knowledge, and nurtured by care. We illustrate this model with case studies from Guyana and the Democratic Republic of Congo, drawing on lessons learned to develop a generic theory of change that can be adapted to guide the development of tailored interventions for the consolidation of stewardship in the context of CBWM. We argue that CBWM initiatives involving Indigenous peoples and local communities cannot view wildlife management as disconnected from strategies for self-determination within a context of reappropriation of customary lands and may require enabling actions to recover agency and place-based care through reconnection to the territory and mentored practice.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.042 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".