Bibliographic record
Abstract
The aim of the study is to compare the satisfaction levels of the participants, participating in the sports festival according to some variables. The survey was conducted to 1,274 people randomly selected among approximately 55,000 participants who participated in the sports festival organized by a municipality in Denizli-Turkey. As a data collection tools “Personal Information Form” prepared by the researchers and the “Festival Satisfaction Survey” which was developed by Yoon et al. (2010) and adopted into Turkish by Tayfun and Arslan (2013). The data collection forms applied to the participants during the festival and the data were checked in terms of normal distributions in the statistical analysis program and analyzed by using independent samples T test and ANOVA analysis methods. As a result of the analysis, male participants perceived a significant level of satisfaction in the sub-dimensions of program, value, satisfaction and loyalty from the questionnaire. A significant difference was found in the sub-dimensions of knowledge, value, satisfaction, and loyalty in the examination conducted by occupational groups. It is seen that this difference is caused by the public employees’ higher satisfaction level. Another hypothesis of the study was to compare the satisfaction of participants according to their educational status. Significant differences were found in the sub-dimensions of knowledge, value, satisfaction, loyalty. It is seen that reason for the statistically meaningful difference, primary school graduates feel lower satisfaction from the festival in terms of education level. As a result, it is thought that participants of the sport festivals feel different satisfaction in terms of their gender, education status and working situation and while planning that kind of events these characteristics must be taken into consideration.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".