Whether Loyalty to a Football Club Can Translate into a Political Support for the Club Owner: An Empirical Evidence from Thai League
Bibliographic record
Abstract
The participation of politicians and their kin in the sport of football, as presidents of football clubs, in the past many years has been widely criticized as a use of the football clubs as tools to gain popularity and, possibly, a political base or a voting bloc for these politicians. This research is conducted in order to (1) study the loyalty level towards football clubs and the corresponding political supports expressed towards the football club executives and (2) examine the relationship between such demographic factors as gender, age, educational level, occupation, income, duration of being a fan, as well as loyalty to the football club and the aforementioned political supports, by collecting data from fans of five football clubs competing in the Thai League during the 2016 season. Including 385 fans, the data are collected using questionnaire, and then analyzed in terms of frequency, percentage, mean, standard variation, and Pearson’s correlation coefficient analysis with the significance level set at five percent. The research finds that overall the fans of all five clubs are highly loyal to the club and express a moderate political support for the club executives. It also finds that gender, age, and education have no relationship to the political support, while occupation, income, duration of being a fan, and especially loyalty to the football club are correlated with the political support. This result confirms the hypothesis that loyalty to a football club can, in fact, potentially translate into a political support for the politicians who are also the owners of the football clubs.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".