The Emergence of a Professional Logic in English Rugby Union: The Role of Isomorphic and Diffusion Processes
Bibliographic record
Abstract
The organizational field that constitutes English rugby union has undergone substantial change since 1995. This paper builds on earlier work by O’Brien and Slack (2003a) that established that a shift from an amateur to a professional dominant logic in English rugby union took place between 1995 and 2000. Utilizing ideas about institutional logics, isomorphism, and diffusion, the current paper explores how this shift in logics actually evolved. Data from 43 interviews with key individuals in English rugby union form the main data source for the study. The results show that isomorphic change in accord with a new professional logic diffused throughout the field by way of three distinct diffusion patterns: status driven, bandwagon, and eventually, the social learning of adaptive responses. An initial period of high uncertainty, intense competitive pressures, and sustained financial crises resulted in unrestrained mimesis in the first two seasons of the professional era. However, this gave way in the third season to increased interorganizational linkages, coalition building, and political activity that promoted normative and coercive pressures for a consolidation of the game’s infrastructure and future development.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".