The Impact of World Ranking Systems on Graduate Schools of Business: Promoting the Manipulation of Image over the Management of Substance
Bibliographic record
Abstract
This essay explores and examines how rankings and league tables have played (and continue to play) a major andconsequential role in how contemporary business schools manage their affairs. It introduces and advances theproposition that rankings promote the short-term manipulation of public reputation (image) projected by businessschools at the expense of the long-term investments in quality improvement. When schools shift scarce resources toactions aimed at enhancing their public image in the short-term, the consequences for the quality of the professionaleducation is significantly compromised in the long-term to the detriment of the constituencies that they serve. Whilethis paper focuses mainly on business schools in the United States and Canada, where this author has experiencedthese consequences first-hand, the effects are similar if perhaps less dramatic, for those professional businessprograms located in higher education institutions operating in the United Kingdom and Europe. While rankingsystems are not going away anytime soon, some potential ways are identified for business schools to escape thedeleterious and perverse effects of being captive players in the deadly rankings game.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".