Causes of the Decline of the Business School Management Science Course
Bibliographic record
Abstract
The business school management science course is suffering serious decline. The traditional model- and algorithm-based course fails to meet the needs of MBA programs and students. Poor student mathematical preparation is a reality, and is not an acceptable justification for poor teaching outcomes. Management science Ph.D.s are often poorly prepared to teach in a general management program, having more experience and interest in algorithms than management. The management science profession as a whole has focused its attention on algorithms and a narrow subset of management problems for which they are most applicable. In contrast, MBA's rarely encounter problems that are suitable for straightforward application of management science tools, living instead in a world where problems are ill-defined, data is scarce, time is short, politics is dominant, and rational “decision makers” are non-existent. The root cause of the profession's failure to address these issues seems to be (in Russell Ackoff's words) a habit of professional introversion that caused the profession to be uninterested in what MBA's really do on the job and how management science can help them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".