Examination of the Attitudes of Adanaspor’s and Adana Demirspor’s Supporters Relevant to Fanaticism
Bibliographic record
Abstract
The purpose of this research is to examine the behaviors of Adanaspor’s and Adana Demirspor’s supporters relevant to fanaticism, and to reveal factors causing partisanship and identification levels of partisanship with psycho-social aspects in the context of football fanaticism and supporter identity. The model of this study is screening. 160 supporters in total, as being 80 Adanaspor’s supporters and 80 Adana Demirspor’s supporters, are constituting the study group. In this study, Football Supporters Fanaticism Scale developed by Taşmektepli et al. (2014) has been used as data collection tool. The analysis of data has been performed by SPSS 22 packaged software. First, percentage and frequency analysis have been performed for age, educational background and profession information of each participant, and distribution (Sample K-S) and homogeneity (ANOVA) analyses have been performed in the determination of difference. As per the results of the research, while they are definitely agreeing with the judgments of “I get very angry at the comments of the speaker during live broadcasts which are against the team”, “I go to the stadium for watching my team’s matches”, “I go to the match with clothing and materials indicating the symbols of my team”, “I join all the cheering at the stadium”, “I try to make the individuals or children—who don’t support a team—the supporters of my team”, they are definitely not agreeing with the judgments of “I may throw foreign bodies to field when I get angry during the match”, “My team should try all the means including exceptions in order to win the match”, “I may sometimes enter the field if the game has gone off the rails”. The comparison of the teams that the supporters support and of their fanaticism attitudes has been made, and significant difference has been determined in favor of Adanaspor in the judgments of “I get very angry at the comments of the speaker during live broadcasts which are against the team” and “I go to the match with clothing and materials indicating the symbols of my team”.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".