Insights on fostering the emergence of robust conservation actions from Zimbabwe's CAMPFIRE program
Bibliographic record
Abstract
One strategy to address threats to biodiversity in the face of ongoing budget constraints is to create an enabling environment that facilitates individuals, communities and other groups to self-organise to achieve conservation outcomes. Emergence (new activities and initiatives), and robustness (durability of these activities and initiatives over time), two related concepts from the common pool resources literature, provide guidance on how to support and enable such self-organised action for conservation. To date emergence has received little attention in the literature. Our exploratory synthesis of the conditions for emergence from the literature highlighted four themes: for conservation to emerge, actors need to 1) recognise the need for change, 2) expect positive outcomes, 3) be able to experiment to achieve collective learning, and 4) have legitimate local scale governance authority. Insights from the literature on emergence and robustness suggest that an appropriate balance should be maintained between external guidance of conservation and enabling local actors to find solutions appropriate to their contexts. We illustrate the conditions for emergence, and its interaction with robustness, through discussing the Communal Areas Management Programme for Indigenous Resources (CAMPFIRE) in Zimbabwe and reflect on efforts at strengthening local autonomy and management around the world. We suggest that the delicate balance between external guidance of actions, and supporting local actors to develop their own solutions, should be managed adaptively over time to support the emergence of robust conservation actions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".