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Record W2612103955

Enforcing Private Environmental Governance Through Community Contracts

2017· article· en· W2612103955 on OpenAlexaff
Kristen van de Biezenbos

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicProperty Rights and Legal Doctrine
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEnforcementBusinessCorporate governancePublicityObligationAccountabilityCompliance (psychology)Environmental governanceContext (archaeology)Environmental complianceHarmGood governanceAccountingPublic relationsFinancePolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

One of the greatest challenges in private environmental governance is enforceability: if we are depending upon corporations like banks and insurance companies to formulate and implement standards that curb greenhouse gas emissions and discourage environmental harm, how do we know whether these firms are living up to those commitments? And, if they are not living up to those commitments, what is the remedy? Using the Carbon Principles as an example, this essay explores whether coupling private environmental governance initiatives with community contracts could strengthen the effectiveness of and provide a level of enforcement, or at least a measure of public accountability, for both. To accomplish this, compliance with specific private environmental governance standards and principles could be expressly included in the community contract, making compliance an obligation of the corporate party. This would not completely solve the problem of enforcement in the context of private environmental governance measures, since there is still the issue of monitoring and compliance. However, it would at least make the public aware of these standards and could create “soft” enforcement by connecting the failure to comply with negative publicity and public opinion.

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 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
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 head0.025
GPT teacher head0.294
Teacher spread0.269 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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