‘Integrity, Sportsmanship, Character’: Baseball’s Moral Entrepreneurs and the Production and Reproduction of Institutional Autonomy
Bibliographic record
Abstract
Sociologists have long argued that institutions like religion or economy can become relatively distinct spheres that facilitate and constrain action, goal setting, and decision-making. But, few empirical studies have looked closely at how institutions become relatively distinct cultural and structural domains. This paper examines how institutional entrepreneurs—in this case, Major League Baseball (MLB) sportswriters—build and sustain institutional boundaries by considering how they create a distinct cultural discourse that infuses baseball places, times, and events with culturally distinct meanings. Drawing from sportswriters’ columns, documentaries, and monographs written on baseball, we show that MLB entrepreneurs have developed and disseminated a discourse oriented around the generalized medium of sport exchange, interaction, and communication: competitiveness. Using these data, the paper below examines how this medium becomes quantified and embodied in tangible and intangible forms. Additionally, the paper draws on sports columns that illustrate how MLB entrepreneurs protect the autonomy of a sacred core (the Hall of Fame) from internal threats (gambling and performance-enhancement drugs) and external corruption (the influence of money). The paper ends with a discussion of implications for the applicability of the findings to other sports and institutional domains.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".