Free-Riding on Power Laws: questioning the validity of the Impact Factor as a measure of research quality in organization studies
Bibliographic record
Abstract
The simplicity and apparent objectivity of the Institute for Scientific Information’s Impact Factor has resulted in its widespread use to assess the quality of organization studies journals and by extension the impact of the articles they publish and the achievements of their authors. After describing how such uses of the Impact Factor can distort both researcher and editorial behavior to the detriment of the field, I show how extreme variability in article citedness permits the vast majority of articles— and journals themselves—to free-ride on a small number of highly-cited articles. I conclude that the Impact Factor has little credibility as a proxy for the quality of either organization studies journals or the articles they publish, resulting in attributions of journal or article quality that are incorrect as much or more than half the time. The clear implication is that we need to cease our reliance on such a non-scientific, quantitative characterization to evaluate the quality of our work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.489 | 0.884 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.007 | 0.104 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".