Cultural group selection and the design of REDD+: insights from Pemba
Bibliographic record
Abstract
Evolutionary analyses of the ways humans manage natural resources have until recently focused on the costs and benefits of prudent resource use to the individual. In contrast, the fields of environmental resource management and sustainability focus on institutions whereby successful practices can be established and maintained, and the extent to which these fit specific environmental conditions. Furthermore, recent theoretical work explores how resource conservation practices and institutions can emerge through co-evolutionary processes if there are substantial group-level benefits. Here we examine the design of a prominent yet controversial institutional intervention for reducing deforestation and land degradation in the developing world (REDD+), and its ongoing implementation on Pemba Island (Zanzibar, Tanzania) to determine the extent to which the features of REDD+ might allow for the endogenous adoption of sustainable forest management institutions. Additionally, we consider factors that might impede such outcomes, such as leakage, elite capture, and marginal community participation. By focusing on prospective features of REDD+ design that could facilitate the spread of environmentally sustainable behavior within and between communities, we identify distinct dynamics whereby institutional practices might coevolve with resource conservation practices. These insights should contribute to the design of more effective forest management institution in the future.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".