The Key-Attributes That Influence the Fans’ Perceptions of the Corinthians’ Ecosystem
Bibliographic record
Abstract
Objective: This article aims to understand and analyse which fans’ attributes most infers in their view concerning the sports ecosystem of the Sport Club Corinthians for sporting events. Methodology: an exploratory research was carried out consisting 78 topics using the Likert scale to be administered to 180 sports fans in 3 matches between February and March 2017. The analysis procedure followed three steps: (i) calculating the chi-square testes cross tables; (ii) selecting the topics which achieved less than 5% significance; (iii) and identifying that group of fans’ attributes that are most similar and most divergent. Findings: monthly salary is the most critical fan attribute; monthly attendance is the second fan attribute most divergent. Fans understand that the stadium as well as partnerships and sponsorships as the critical dimensions of the Corinthians’ ecosystem. Conclusion: Therefore, 2 out of 3 hypotheses were confirmed. Besides, issue as to gender is not a critical fans’ attributes for the Corinthians’ marketers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".