The World Economic Forum Principles on "Climate Governance on Corporate Boards": can soft law help to face climate change around the world?
Bibliographic record
Abstract
Climate change is a financial factor that carries with it risks and opportunities for companies. To support boards of directors of companies belonging to all jurisdictions, the World Economic Forum issued in January 2019 eight Principlescontaining both theoretical and practical provisions on: climate accountability, competence, governance, management, disclosure and dialogue. The paper analyses each Principle to understand scope and managerial consequences for boards and to evaluate whether the legal distinctions, among the various jurisdictions, may undermine the application of the Principles or, by contrast, despite the differences the Principles may be a useful and effective guidance to drive boards' of directors' conduct around the world in handling climate change challenges. Five jurisdictions are taken into consideration for this comparative analysis: Europe (and UK), US, Australia, South Africa and Canada. The conclusion is that the WEF Principles, as soft law, is the best possible instrument to address boards of directors of worldwide companies, harmonise their conduct and effectively help facing such global emergency.
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.020 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".