“Physics envy” in organisation studies: the case of James G. March
Bibliographic record
Abstract
Purpose This article aims to propose a critical review of James G. March’s research in and particular its consistency with its epistemological and psychological underpinnings. Design/methodology/approach The paper proposes a textual and conceptual analysis of James G. March’s study. Findings The article argues first that March’s study exemplifies the “physics envy” typical of management and organisation studies scholars since the early 1960s. Second, evidence is presented that March’s conclusions, irrespective of their legacy on management and organisation studies, were not developed along and were not consistent with the foundations that March espoused and advocated during most of his career. As a result, the implications of his conclusions are uncertain. To his credit, however, there are reasons to believe that, towards the end of his career, March came to recognise the limitations of his scholarship. Further, he indicated an alternative avenue for organisation studies which eschews the shortcomings of positivist and post-modern research. Research limitations/implications Although centred on March’s work, the argument presented is relevant to psychology, organisations, choice, the nature of knowledge, the limitations of positivism and post-modernism. Originality/value The paper balances the perspective offered by recent celebratory reviews of March’s study.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.038 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| 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 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".