FEATURE ARTICLE Making “The List” Business School Rankings And The Commodification Of Business Research1
Bibliographic record
Abstract
N HIS New Yorker essay on college admissions practices, Gladwell (2005) reflects on how he chose which post-secondary school to attend. He recalls that: In Ontario, there wasn’t a strict hierarchy of colleges. There were several good ones and several better ones and a number of programs…that were world class. But since all col-leges were part of the same public system and tuition everywhere was the same (about a thousand dollars a year, in those days), and a B average in high school pretty much guar-anteed you a spot in college, there wasn’t a sense that anything great was at stake in the choice of which college we attended. (n.p.) Obviously, higher education has seen many changes in the past twenty years. Not only have universities become known for specific areas of excellence, but business schools in particular have become widely differentiated. The “stakes ” have certainly changed. Perhaps one of the most noticeable changes in recent years has been the appearance of multiple school rankings, generated by popular press periodicals such as MacLean’s magazine in Canada, and US & World News Report in the United States. These publications typically create special issues devoted to assessing various post-secondary institutions according to a wide number of criteria, including innovativeness, reputation, and class sizes. Although many educational programs have been ranked, business school rankings appear to be particularly popular; media rankings of undergra-duate, MBA, EMBA, and executive development programs have been conducted by Business-
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.094 | 0.031 |
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".