Co-regulating corporate social responsibility : government response to forest certification in Canada, the United States and Sweden
Bibliographic record
Abstract
The emergence of private environmental governance has been interpreted in the policy and global governance literature as a “retreat of the state” or “governance without government”. However, the most established example of a corporate social responsibility (CSR) standard, forest certification, reveals governments endorsing, enabling and even mandating certification. Forest certification demonstrates that the state is not in retreat, but has simply shifted its role towards co-regulation. Despite the increasing evidence, scholars have largely ignored the significance of this transformation. This dissertation addresses this critical knowledge gap by developing the governance concept of CSR co-regulation, which serves to explain how governments are harnessing private rule-making authority alongside state regulation. Through a comparative case study drawing on more than 120 interviews, the research evaluates how and why governments within the world’s leading certified nations (Canada, the United States and Sweden) have responded to forest certification, and the implications for forest governance. The results show that these governments are increasingly engaging in certification through a range of co-regulatory approaches that complement, rather than substitute for forest laws. While the rationale for co-regulation are similar across the case study jurisdictions, government co-regulatory responses have differed as influenced by socio-political, economic and environmental factors within the local context. The cases also highlight how certification co-regulation benefits forest administration, decision-making processes, and policy outcomes and suggest that governments are engaging in certification for other than market-driven reasons. The evidence challenges the theory of “non-state market-driven” governance, demonstrating that certification is more accurately classified as a co-regulatory forest governance mechanism. Three new analytical tools are presented to evaluate the co-regulatory arrangements, and establish a framework to facilitate future research in this area. As well, the findings offer practical guidance to policy makers seeking new adaptive governance approaches to address complex sustainability challenges.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.028 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".