Building the Forest-Climate Bandwagon: REDD+ and the Logic of Problem Amelioration
Bibliographic record
Abstract
For those championing an international institutional solution to climate change, the forest-climate linkage through reduced emissions from deforestation and forest degradation and forest enhancement (REDD+) may be one of the most promising strategic linkages to date. Following a series of forest-focused interventions that did not live up to their promise, global forest politics have now, through REDD+ deliberations, been institutionally subsumed into the climate regime. We argue that to realize its potential, REDD+ policy mechanisms must be careful to move away from the commodification of forest stewardship that reinforces short-term strategic positions of powerful producing and consuming interests whose current activities are the culprits of global forest decline. To achieve such an outcome, we argue that institutions must develop on the basis of a “logic of problem amelioration” in which the rationale for achieving clearly defined environmental and social goals is rendered transparent. This could be achieved through the formalization of a “dual effectiveness test” in which interventions are evaluated for their potential to simultaneously ameliorate both global climate change and forest degradation.
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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 | 0.041 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.089 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".