Optimal Distinctiveness Revisited: Discursive Strategies of Intercategorical Positioning of New Market Categories
Bibliographic record
Abstract
Research on optimal distinctiveness is situated at the intersection of institutional theory and strategic management. Using Mixed Martial Arts as an explanatory case study, we examine its evolution as a distinct market category of combat sport within the broader institutions of sport and society. We identify three intercategorical positioning benchmarks that serve as evaluative criteria for assessing optimal distinctiveness—broader societal values, values of umbrella categories, and norms of horizontal peer categories. Drawing on these benchmarks, we propose a layered strategic framework that comprises three foundational positioning strategies—excessive differentiation, moderate differentiation, and over conformity. These foundational strategies are configured and enhanced through two overarching configural strategies; moderating integration involves complementary coupling and multilevel positioning, while contextual adaptation encompasses strategic categorization and intertemporal positioning. Our findings challenge traditional conceptualizations of optimal distinctiveness by illustrating how new sports rely on dynamic, context-dependent strategies to establish legitimacy and ensure category viability.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".