Achieving Sustainability Through Market Mechanisms
Bibliographic record
Abstract
For over a generation, government, business, and non-governmental organizations have been increasingly turning to “market mechanisms” to promote sustainable forestry around the world. In contrast to traditional “command and control” approaches in which governments require and enforce a specified behavior through mandatory regulations (Taylor et al. 2012) market mechanisms attempt to create economic incentives for firms, managers and individuals, to behave in ways that improve environmental stewardship and foster social values. Given that many have criticized previous efforts to combat deforestation for falling short of intended goals, the role and use of market mechanisms has become of great interest to practitioners, scholars and policy makers (McDermott 2014a). The direct impact of market mechanisms is not always easy to measure and is rarely immediate (Auld and Cashore 2012). Market mechanisms also interact with public policies in myriad ways. Depending on design and trajectory, these efforts can lead to standards that ‘ratcheting up’, ‘ratcheting down’, or produce status quo forestry practices. The purpose of this chapter is to shed light on these processes by exploring the multiple pathways through which market mechanisms exert influence.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".