Bangkok Men’s Attitudes towards Marital Rape
Bibliographic record
Abstract
This research aims (i) to explore Bangkok men’s understandings of section 276 of the criminal code; (ii) to examine the attitudes on marital rape as perceived by men in Bangkok and to conduct a comparative study on the observed attitudes using various demographic assessment factors, namely, age, marital status, occupation, and income level; (iii) to investigate the correlations between the level of understandings of the relevant law and the attitudes of men living in Bangkok on marital rape by employing quantitative research method, with the use of questionaries that would collect data from 280 research subjects. The data analysis is carried out using frequency, mean, percentage, one-way analysis of variance, least significant difference (LSD), and Pearson product-moment correlation coefficient analysis with significance level 0.5. The research findings indicates that the subjects possess the least understandings of section 276 of the criminal code ( = .15) and contain average level of attitudes with respect to the issue of marital rape ( = 3.19). When examining with different assessment factors, it is found that the subjects bare an attitude of cognition at low level ( = 2.22), of feelings at average level ( = 3.36), and of action tendency at considerable level ( = 3.98). Results from the comparative analysis on different levels of attitudes over the issue of marital rape, using different demographic assessment factors, reveal that the difference in income generates different level of attitudes whereas the differences in age, marital status and occupation bare relatively similar level of attitudes. Lastly, no correlation is found when looking at the outcome of the correlation analysis between level of understandings and attitudes perceived.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".