Who Fills the Global Governance Gap? Rethinking the Roles of Business and Government in Global Governance
Bibliographic record
Abstract
Political CSR has made great strides towards a better appreciation of the political involvement of corporations in global governance. However, its portrayal of the shifting balance between business and government in the globalized economy rests on a central, yet largely uncontested, assumption: that of a zero-sum constellation of substitution in which firms take on public responsibilities to fill governance gaps left by governments. This conceptual paper expands the political CSR perspective and makes three contributions to the debate on the political role of business and the role of government in global governance. First, it deconstructs the problematic assumptions underlying the zero-sum notion of governance gaps filled by corporations. Second, it offers a variable-sum mapping of how private and public authority interact in global governance where substitution is only one of four constellations. The mapping identifies ‘soft steering’ as a prominent mode of governments governing business conduct. Third, the paper theorizes ‘orchestration’, a ‘soft steering’ tool discussed in the global governance literature, from an organizational, corporate perspective. It identifies the mechanisms through which orchestration may address the barriers to corporate engagement with the public good and applies these mechanisms to the case of the Global Reporting Initiative.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.016 | 0.034 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".