Publishing in the Majors: A Comparison of Accounting, Finance, Management, and Marketing*
Bibliographic record
Abstract
Abstract Business schools evaluate publication records, especially for the promotion and tenure decision, by comparing the quality and quantity of a candidate's research with those of peers within the same discipline (intradisciplinary) and with those of academics from other business disciplines (interdisciplinary). A recently developed analytical model of the research review process provides theory about the norms used by editors and referees in deciding whether to publish research papers. The model predicts that interdisciplinary differences exist in quality norms, which could result in disparity among business disciplines in the number of top‐tier articles published. I examine the period from 1980 to 1999 and, consistent with the theory, find that significant differences exist in the number of articles and proportion of doctoral faculty who published in the “major” journals in accounting, finance, management, and marketing. Most notably, the proportion of doctoral faculty publishing a major article is 1.4 to 2.4 times greater in the other business disciplines than in accounting (depending on the set of journals). The theory also predicts an upward drift over time in the quality norms used by referees. Consistent with a drift, the number of articles published has declined substantially in marketing and, to a lesser extent, in the other business disciplines.
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".