Political Ideology and Prioritization of Qualities for Boyfriends-to-be Among Thai Female University Students
Bibliographic record
Abstract
This research aims to (1) examine the political ideology and prioritization of qualities for men to be chosen as a boyfriend, (2) compare such prioritization among individuals by considering their personal factors, including class years, majors, hometowns, parents’ occupations, and household incomes, and (3) test the relationship between the political ideology and such prioritization. The research is conducted by collecting data from 400 female students of a private university in Pathumthani, Thailand who registered in the final semester of the 2016 academic year. The data are collected via questionnaires, and statistically analyzed by finding the frequencies, percentages, means, and standard deviations as well as by adopting the methods of one-way analysis of variance (ANOVA), Tukey’s Pairwise Comparison Test, and Pearson’s correlation coefficient analysis, with the statistical significance set at the 5-percent level. The results show that overall the sample’s political ideology leans slightly towards liberalism, and the sample gives a moderate priority to the qualities of men to be chosen as a boyfriend. The quality to which the sample gives the top priority is the personal characters of the men. It is also found that the five personal factors also affect the prioritization of qualities for men to be chosen as a boyfriend, and that the political ideology and the prioritization of qualities for the boyfriend-to-be are only weakly related.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".