Smoke, Mirrors, and Impact Factor: How Management Scholars Undermine a Managerial Metric
Bibliographic record
Abstract
The authors analyze leading management journals of the Academy of Management (the Academy of Management Journal, the Academy of Management Review, the Academy of Management Learning and Education, and the Academy of Management Perspectives, as well as the Journal of Management), collecting and analyzing information on the managerial research assessment tool of impact factor (IF) during two time periods—from 1997 through 2005 and from 2006 through 2019. The authors capture the changing nature of journal strategies, examining the number of references, self-citations, and cross-citations. The study shows a general increase in the number of references used, as well as self-citations and cross-citations, resulting in a corresponding IF gain. The evidence suggests some limitations to adopting performance metrics in academia. Thus, academic managers, editors, and authors who focus on IF may be overestimating its impact, obscuring institutional attempts to measure academic research performance.
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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 | 0.061 | 0.307 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".