Evaluating new and innovative models of management education
Bibliographic record
Abstract
This chapter is devoted to a critical examination and evaluation of a number of new models and interesting new approaches to management education that have been advocated both by deans (e.g. Richard Lyons at Haas, Berkeley, in the US, and before him Laura Tyson, and Roger Martin at Rotman, Toronto, in Canada) and critics (e.g. Henry Mintzberg at McGill, Montreal, Canada). We believe that the organising framework of Figure 4.4, and Simon’s careful insights, should provide a basis for our model review and analysis of the philosophy underlying each model. Despite the somewhat unfulfilled promise of management education (Thomas, 2012), there has been considerable investment in new business models for its future development. Indeed, Professors Datar, Garvin and Cullen (2010) provide an exhaustive review of current curricula trends. Prompted by the growing scrutiny of MBA programmes, they started an ambitious and wide-ranging three-year research project on MBA programmes to coincide with the one-hundredth anniversary of Harvard Business School. They examined a range of secondary data sources, interviewed leading business school deans and corporate executives, and outlined clearly the curricula developments at around a dozen leading schools, focusing particularly on programmes at the Center for Creative Leadership, Chicago, Harvard, INSEAD, Stanford and Yale.
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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 | 0.044 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".