The Search for Synergy between Institutions and Multinationals: Institutional Uncertainty and Patterns of Internationalization
Bibliographic record
Abstract
The debate on globalization has long been characterized by theses of institutional convergence and divergence. The emergence of Anglo-Saxon shareholder capitalism as the dominant paradigm since the start of the 1990s is associated with the pursuit of global strategies by Multinational Enterprises (MNEs) and the consolidation of a multilateral trade regime. Yet the link between actual MNE strategies and developments in the institutional arena remains an understudied phenomenon. Tensions between multiple levels of institution building – unilateralism, regionalism and multilateralism – create an environment of strategic uncertainty for MNEss. Consequently, MNEs’ actual international strategies reveal much about perceptions of the institutional environment in which they operate and allows for the documentation of more subtle paradigm shifts. The internationalization strategies pursued by MNEs from the Triad over the 1990s reveal that a multilateral strategic reality was anticipated by only an elite few, while the vast majority of firms operated in a unilaterally- or at best regionally-determined institutional environment. This contribution suggests that institutional restructuring is multifaceted and sometimes contradictory, casting a new and more subtle light on the globalization debate.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".