A Reply to “Business Marketing in Master's Programs: A Part of the Fabric”, But What If the Suit Differs?
Bibliographic record
Abstract
ABSTRACT. In this reply to James Narus, James Anderson and Earl Honeycutt (NAH) we elaborate on the journey, a 25-year process, in teaching Business Marketing at Ivey. We further discuss the goal to ele-vate Business Marketing knowledge in the core of the program, rather than to build a strong specialization in the area. Inspired by NAH’s re-ply, we then comment on the root causes of lack of progress at some schools in bringing more Business Marketing into master’s programs, and we address the question, “What about those schools where the char-acteristics are different from Ivey, i.e., how far can our ideas be general-ized? ” doi:10.1300/J033v14n01_07 [Article copies available for a fee from The
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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 | 0.023 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".