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Can Non‐state Governance ‘Ratchet Up’ Global Environmental Standards? Lessons from the Forest Sector

2007· article· en· W2139195110 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReview of European Community & International Environmental Law · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBoycottIncentiveBusinessCorporate governanceState (computer science)Environmental governanceCertificationConventionCertified woodSummitEarth SummitScholarshipPublic economicsSustainable developmentPolitical scienceEconomicsMarket economyFinancePoliticsLaw

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.271
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it