Investors and global governance frameworks: broadening the multi-stakeholder paradigm
Bibliographic record
Abstract
Introduction Companies play an increasingly important role in the global economy. With this growth has emerged a strong view that companies share in responsibilities traditionally assigned to governments – such as those relating to human rights and the environment (Ambachtsheer 2011). A wide range of norms, codes of conduct and conventions have emerged to translate this broadening acceptance of extended corporate responsibility into policy and practice (see Appendix 30.1). Traditionally, conventions were developed by multilateral institutions and targeted for ratification by national governments. More recently, a broader range of stakeholders have become involved in developing and supporting conventions under the espoused benei ts of “multi-stakeholder processes” (Vallejo and Hauselmann 2004). This has resulted in a shift from legislative foundations towards the emergence of “soft law” approaches to regulating behavior, tending to take the form of nonbinding and voluntary codes of conduct. Multi-stakeholder processes have gained their standing as valid mechanisms to develop and implement codes of conduct in part because they include input from a broad range of stakeholders in their design, implementation and oversight. This chapter focuses on one stakeholder which is largely absent from the analysis of these processes – investors.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".