Teaching Corporate Governance in an <scp>MBA</scp> Class: An Academic Note*
Bibliographic record
Abstract
ABSTRACT Corporate governance is a new and emerging discipline in academia. The discipline of corporate governance has increased in importance, driven mostly by multiple financial scandals. These scandals have motivated regulators and the business community to require business professionals who are properly trained in various aspects of corporate governance. The problem, however, is that corporate governance covers many different subjects, such as finance, strategy, and law. This problem of multidimensionality of corporate governance is coupled with the fact that it is a field of study that has rapidly changed to keep up with changing regulations and the changing needs of the business community. These problems make the task of teaching corporate governance challenging for educators. Notwithstanding these challenges, business programs for MBA and CPA students are incorporating corporate governance into their programs. Therefore, our objective is to provide reflections on our three‐year experience of teaching corporate governance. We highlight the challenges that we experienced, such as the diversity of the clientele and the multidisciplinarity of corporate governance, and we discuss how we addressed these challenges. We also identity opportunities we used to develop our teaching approach, such as team teaching and using data from real companies. We present our reflections with the help of education frameworks that should help the community of educators if faced with challenges similar to ours.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".