Regional Strategy: Assessing Past Achievements and Charting the Future for MNE Research
Bibliographic record
Abstract
Our panel discussion will thoroughly examine the current state of knowledge in regional multinational enterprise (MNE) strategy, covering critical aspects like the regionalization vs. globalization debate, the impact of multinationality on MNE performance, regional strategies and subsidiary management, and the significance of institutional complexity. Beyond assessing existing knowledge, the panelists will also present future research questions, providing valuable guidance for scholars in advancing our understanding of regional MNE strategy. Recognizing the dynamic nature of international strategic management, our panel will incorporate emerging themes such as climate change, sustainability, and resilience into the discussion, ensuring our exploration remains at the forefront of addressing contemporary challenges. Leveraging their expertise, the panelists will organize presentations around key future research questions, offering a structured and insightful exploration of the pivotal issues shaping the future of regional MNE strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".