Can Non‐state Governance ‘Ratchet Up’ Global Environmental Standards? Lessons from the Forest Sector
Bibliographic record
Abstract
The failure of the worlds’ governments to agree on a binding global forest convention at the 1992 Rio Earth Summit led many leading environmental groups to advance eco‐labelling ‘forest certification’ programmes that, they hoped, would achieve greater success in implementing sustainable forest management. Eschewing traditional State‐centered authority, supporters of this ‘non‐State market driven’ (NSMD) approach turn to customers of wood products to create compliance mechanisms, either through positive incentives such as market access or price premiums, or negative incentives such as ‘direct targeting’ or ‘boycott’ campaigns. Understanding how such systems might ‘ratchet up’ global forestry standards, we argue, requires that existing scholarship place greater attention on the role of public policies in helping to facilitate the impacts of private solutions. Specifically, we argue that scholars and practitioners need to assess strategic decisions not only on the basis of their appropriateness at present, but what they might do to trigger a global ‘race to the top’ at a later time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".