How Do States Benefit from Nonstate Governance? Evidence from Forest Sustainability Certification
Bibliographic record
Abstract
Forest sustainability certification is emblematic of governance mechanisms associated with neoliberal state reforms. Despite being conceived as a means of compensating for the unwillingness or inability of states to regulate forest practices, in practice, forest certification has come to entail complex and hybrid relationships between private-sector, civil society, and government actors. Indeed, states have increasingly embraced certification as a means of complementing or even supplanting traditional forms of governmental regulation of the forest sector. Yet processes of neoliberalization imply both an expansion of opportunities for hybrid governance and a weakening of the state capacity that is often needed for successful implementation of certification initiatives. We analyze the complex relationships between neoliberalization, state capacity, and certification through two contrasting cases in Wisconsin, United States, and Entre Ríos, Argentina. Our findings illustrate the tensions within broadly neoliberal and postneoliberal regimes and highlight the persistence of long-standing patterns of state-led environmental governance.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".