Impact of Cross-national Differences in the Human Rights and Acquisition Experience on Emerging Market Multinationals’ Cross-border Acquisitions
Bibliographic record
Abstract
This year's conference theme, "The Dynamics of International Business," is timely given our current global situation.Today, in this Era of Great Transformation amidst global crises, we face the dual challenge of balancing the goals of durable economic development for a sustainable world.We are reminded of the role of international business and international management in harnessing a swift-moving and increasingly diverse global economy for both social and environmental benefits.We hope the papers and discussions here contribute to solving a myriad of international issues.The Republic of Korea, where we assemble today, is a sign of this swift-moving and increasingly diverse global economy.Despite being a poor and underdeveloped country after the war, Korea now is a developmental benchmark to others showing what is possible.The UN Conference on Trade and Development (UNCTAD) classified South Korea as a fully 'developed' economy in 2021.This is the first time this UN agency upgraded a member state's status since its establishment in 1964.So, as you explore our country, we hope scholars use their internationally comparative mindsets to think about Korea's unique dynamics and choices of cultural, organizational, and development policy that helped the country succeed so quickly.If this is your first journey to Korea, you probably noticed our country and culture is far more 'saturated' in the daily use of digital networks than many other nations.However, you may not know that Seoul from 2018 was rated as the world's fourth largest metropolitan economy-only after Tokyo, New York City, and Los Angeles-with an annual GDP of $895 billion US dollars.Major global manufacturers headquartered in Seoul include Samsung, Hyundai, SK and LG.In short, coming to Korea is to live and breathe in a global high-tech culture of the future, yet with a very ancient culture just the same.To conclude, I thank the organizing committee and sponsoring organizations for their hard work in making this conference possible.I extend a warm welcome to our distinguished keynote speakers, panelists, and participants.I hope our conference sparks meaningful discussions and a fruitful exchange of ideas.I hope it brings you new insights on the opportunities and challenges of our world's competitive digital globalization in a post-pandemic world.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".