Evaluate the Barriers to Attract Sponsors in the Sport: Industry of Khorasan Razavi
Bibliographic record
Abstract
Purpose: Evaluate the barriers to attract financial supporters in the sport industry of Khorasan Razavi has been done.Methodology:The method of this research is applied and descriptive-comparative type. The statistical population of this research includes all managers of factories and private companies, managers of sport clubs and sport authorities of Khorasan Razavi province which among these the 100 CEOs of factories, 114 club managers and sport chairmanparty have been chosen randomly in Mashhad, Sabzevar, Taybad and Kashmar. Questionnaires of sport financial supporters of Ameri and et al (2009) had been the tools under usedwhich the Cronbach's alpha has been obtained as 0.711 in this study. SPSS19 software has been used in order to evaluate data analysis. Research hypotheses has been evaluated by using independent t-test.Findings: findings showed that the both first and forth hypotheses were confirmed among 4 hypotheses that were tested means that, there is a difference between perspective of managers of private companies and sports managers in relation with barriers of financial supporters and problems related to teams. Conclusion: Obtained results have been explained and compared based on findings of pervious researches. In fact most managers of companies compared with club managers the lack of sponsor from the sports industry have mentioned as a most important factor and problems related to teams including unpopularity of teams and Lack of using players and famous coaches is important factor for lack of attracting financial supporter in Mashhad from perspective of both groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".