How Really Unique Are Emerging Markets and Their Firms?
Bibliographic record
Abstract
Emerging markets (EMs) have become a focal point of academic inquiry and policy interest as they complement and sometimes even challenge established paradigms based on developed economies. International Business scholars, practitioners, and policymakers have long debated how unique emerging markets and their firms are, and if this uniqueness warrants or requires special attention, distinct policies, and new theories. Developed in cooperation with the AIB Emerging Markets Special Interest Group, this AIB Insights special issue explores these questions, articulating novel and actionable insights to understand EMs’ distinctiveness. Our introductory editorial to this issue briefly discusses the nature and dimensions of the uniqueness of emerging markets and their firms. It then surveys the seven issue articles, highlighting their main findings and insights. Given current global developments, the significance of EMs and their firms is likely to grow further, underscoring the importance of continued academic and practical focus on them.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".