Integrating emotions into legitimacy work: an institutional work perspective on new sport emergence
Bibliographic record
Abstract
Research Question The purpose of this study was to explore the role emotions play in new sport emergence.Research Methods A qualitative case study of Mixed Martial Arts (MMA) was undertaken, with content analysis employed to identify emergent themes from an archival database of newspaper articles.Results and Findings Negative emotions were institutionalized into the discourse surrounding early MMA that hindered its legitimation; in order to legitimize the sport, discursive institutional work was undertaken by pro-MMA stakeholders to address existing negative emotions, and create positive new ones.Implications Emotions play a crucial role in new sport emergence; therefore, institutional work aiming at legitimizing a new sport on cognitive grounds alone might be inadequate for the successful emergence of a new sport, without the specific emotion-focused institutional work to disrupt existing negative emotions, and create new positive emotions for the new sport.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.052 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".