A theory‐based framework for understanding the establishment, persistence, and diffusion of community‐based conservation
Bibliographic record
Abstract
Abstract Over decades, biodiversity conservation researchers and practitioners have developed theories and conceptual frameworks to inform the planning, implementation, and evaluation of community‐based conservation (CBC). While a diversity of mechanisms for understanding and supporting CBC has helped tailor approaches to local needs and conditions, the absence of a unified lens to understand CBC has limited the capacity for integrating foundational theory into practice more systemically, and for learning across different projects, stakeholders, and institutions. We introduce a theory‐based framework called “the CBC framework” that draws upon three foundational theories from sociology, economics, and political science to understand the establishment, persistence, and diffusion of CBC. Experience applying aspects of the framework within different conservation organizations demonstrates how this integrative approach can provide a gateway for practitioners to engage with social science theory to understand the status and context of CBC interventions and efforts. For practitioners, scientists, evaluators, and strategists, the framework can guide the design of CBC interventions and monitoring and evaluation systems to facilitate theory‐based learning and enable evidence‐informed decision‐making. Approaches like the CBC framework that facilitate collaborative planning, evaluation, and research can help better integrate social science theory in conservation practice while increasing the capacity for conservation scientists, practitioners, and stakeholders to learn together and adaptively manage CBC to deliver positive results for both people and nature.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".