When Knowledge Wins: Transcending the Sense and Nonsense of Academic Rankings
Bibliographic record
Abstract
“Not everything that can be counted counts, and not everything that counts can be counted.” —Albert Einstein Has university scholarship gone astray? Do our academic assessment systems reward scholarship that addresses the questions that matter most to society? Using international business as an example, we highlight the problematic nature of academic ranking systems and question if such assessments are drawing scholarship away from its fundamental purpose. We call for an immediate examination of existing ranking systems, not only as a legitimate scholarly question vis-a-vis performance—a conceptual lens with deep roots in management research—but also because the very health and vibrancy of the field are at stake. Indeed, in light of the data presented here, which suggest that current systems are dysfunctional and potentially cause more harm than good, a temporary moratorium on rankings may be appropriate until more valid and reliable ways to assess scholarly contributions can be developed. The worldwide community of scholars, along with the global network of institutions interacting with and supporting management scholarship (such as the Academy of Management, AACSB, and Thomson Reuters Scientific) are invited to innovate and design more reliable and valid ways to assess scholarly contributions that truly promote the advancement of relevant 21st century knowledge, and likewise recognize those individuals and institutions that best fulfill the university's fundamental purpose.
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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 | 0.083 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.015 | 0.091 |
| Scholarly communication | 0.041 | 0.054 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".