Attitude toward usage of Patani Malay language in three Southern border provinces
Bibliographic record
Abstract
This study aims to study Thai government policy concerning Patani Malay language. Data used in the analysis are taken from government sources. Also, this research is conducted by using the documentary analysis and supported by the interview and by questionnaires of the officials who are responsible for the security issues in this area local resident in the area. In 2006, the National Reconciliation Commission (NRC) recommended the government to use Patani-Malay Language, or Yawi as the working language in three border provinces as means to relieve violence and help build the security of Thailand, there are many arguments against this proposal. The Privy Council president strongly disagrees with this suggestion on the ground that those three provinces are the part of Thailand, and as such only Thai language will be used in this country. This counter argument is not persuasive because in several countries such as Canada more than one language is used as official language without much of problems. When applied to the three border provinces, where the Patani Malay has been long rooted in their daily lives, the central government from Bangkok might have to reconsider whether to accept the language and local culture of the region. This research scrutinizes opinion of a member of group of people on Patani-Malay language as the bilingual language with Thai under appropriate measures. This idea might lead to help generate national security of Thailand as a whole. This research posits that acceptance of the local culture and language will gain trust of the local citizens on part of the government.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".