Grounding transnational business governance: A political‐strategic perspective on government responses in the Global South
Bibliographic record
Abstract
Abstract Recent scholarship on transnational business governance has begun to examine public‐private interactions and the active role of governments. We make two key contributions that integrate and expand this literature. First, in juxtaposition to functionalist accounts, we foreground the fundamentally political and often contentious character of these interactions. As private transnational governance schemes and standards “hit the ground,” private‐public interactions, we argue, are embedded in national political arenas and tied to domestic distributional struggles among competing regulatory coalitions. Building upon multiple empirical streams of research, we develop a political‐strategic framework that maps the diversity of Southern government responses (substitute, adopt, repurpose, replace, or reject) to transnational private governance. Our framework shows that government responses are a function of both strategic fit with domestic industrial capabilities and structures, and strength of developmental state capacity. Second, our proposed framework adopts the vantage point of Global South governments and industries, particularly how development challenges and strategic options within global value chains affect their understanding of, and responses to, transnational schemes and standards. This is an important corrective to a Northern bias in the private governance literature.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".