How Are Land-Use Multi-stakeholder Fora Affected by Their Contexts?
Bibliographic record
Abstract
Multi-stakeholder mechanisms have been touted as a more democratic and equitable alternative to forest and land use decision-making. It has been argued that these processes do not address power relations and thus maintain the status quo. In this chapter, we examine eight Multi-stakeholder fora in the Peruvian Amazon, half of which have been set up in the Madre de Dios region, and the other half in the San Martin region, both in the Peruvian Amazon. These regions represent two different poles of development paradigms in Peru. While the chapter does not provide a definitive answer around whether multi-stakeholder processes can address power inequalities, three preliminary ideal types are used to analyze these mechanisms, drawn from a realist synthesis review of the literature: decision-making, management and influence. This chapter illuminates how multi-stakeholder fora are affected by their contexts, as well as their process and outcomes.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".