Causes of the Decline of the Business School Management Science Course
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it