‘New Governance’ in European Corporate Law Regulation as Transnational Legal Pluralism
Bibliographic record
Abstract
Abstract The present transformation of European corporate governance regulation mirrors the challenges that have been facing the EU's continuously evolving polity, marked by tensions between centralised integration programmes, on the one hand, and Member State's embedded capitalisms, path‐dependencies and rent‐seeking, on the other. As longstanding concerns with remaining obstacles to more mobility for workers, services, business entities and capital in recent years are aligned with post‐Lisbon commitments to creating the world's leading competitive market, European corporate governance regulation (ECGR) has become exposed to and implicated in a set of highly dynamic regulatory experiments. In this context, ‘New Governance’ offers itself as both a tentative label and immodest proposal for a more responsive and innovative approach to European law making. The following article assesses the recently emerging regulatory forms in ECGR as illustrations of far‐reaching transformations in market governance. The arguable parallels between the EU's regulatory transformation in response to growing legitimacy concerns and the recurring question about whose interests a business corporation is intended to serve, provide the framework for an exploration of current regulatory trajectories in European corporate law that can most adequately be understood as a telling example of transnational legal pluralism.
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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 | 0.022 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.051 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".