International Accreditation of Business Schools in Emerging Markets: A Study of FGV-EAESP and Insper in Brazil
Bibliographic record
Abstract
The globalization of the world economy has had a direct effect on higher education (Canals, 2009; Nerad, 2010). Not only have more companies and organizations from Japan, Europe, Canada and the USA penetrated into the rest of the world, but companies and organiza- tions from the developing countries, especially the emerging markets of Brazil, Russia, India and China (the BRICs), have also become inter- nationalized and compete in global markets. Thus business schools at Institutes of Higher Education (IHEs) worldwide must provide students not only with traditional management skills but also with management skills and perspectives that are relevant to the BRICs and other more pluralistic and multicultural realities (Scott-Kennel and Salmi, 2008; Canen and Canen, 2011; Wallerstein, 2000, pp. 432 33). They must develop strategies both to offer quality education and to compete with other institutes, including strategies involving marketing (Hemsley- Brown and Oplatka, 2006), geographic diversification, and strategic international alliances (Iniguez de Onzono and Carmona, 2007).
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".