Who shuns entrepreneurship journals? Why? And what should we do about it?
Bibliographic record
Abstract
Abstract Some scholars assert that entrepreneurship has attained “considerable” legitimacy. Others assert that it “is still fighting” for complete acceptance. This study explores the question, extrapolating from studies of an “elite effect” in which the publications of the highest ranked schools differ from other research-intensive schools. The most elite business schools in the USA, but not the UK , are found to allocate significantly more publications to mathematically sophisticated “analytical” fields such as economics and finance, rather than entrepreneurship and other “managerial” fields. The US elites do not look down upon entrepreneurship as such. They look down upon journals that lack high mathematics content. Leading entrepreneurship journals, except Small Business Economics Journal (SBEJ) , are particularly lacking. The conclusion argues that SBEJ can help the field’s legitimacy, but that other journals should not imitate analytical paradigms. Plain English Summary Academic snobs shun entrepreneurship journals. A goal for snobs is to exhibit superiority over others. For business professors, one way to do this is with mathematically sophisticated, analytical publications. Entrepreneurship journals, Small Business Economics excepted, do this relatively infrequently. These journals focus on the lives, activities, and challenges of diverse entrepreneurs. In the USA, the most elite business schools, compared with not-quite elite business schools, allocate significantly more of their articles to the journals of analytical fields such as economics, and fewer to entrepreneurship journals. This pattern is not found in the UK, where elites may have other ways to signal superiority. These elites, who accommodate entrepreneurship researchers, could pioneer with outputs of both relevance and scholarly quality, through collaboration between their practice-based and research-based professors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".