Creating a System and Mechanism for Assessing the Quality of Management of the Thai Senate Standing Committee
Bibliographic record
Abstract
The objectives of this study were to 1) create a mechanism to evaluate the management of the Thai Senate Standing Committee and 2) study the problems obstacles and suggestions on how to evaluate the management of the Thai Senate Standing Committee. Three sampling groups in the research; (1), sampling group for creating an assessment system and mechanism (2) sampling group for developing assessment system and mechanism, and (3) sampling group for evaluating the effectiveness of the IQA evaluation criteria. Data were gathered using the questionnaires and the structural interviews. The analysis techniques were content analysis, descriptive statistic using percentage, mean, median and interquartile range. The research findings are as follows; 1) the suitability and the usability of the System and mechanism for assessing the quality of Management of the Thai Senate Standing Committee in overall were at high level; 2) The problems revealed concern the fact that only few senators participated in the quality planning; therefore, others had not been convinced of the assessment system, not to mention that there is no planning revision method. In this aspect, the external quality assurance committees should be called for to ensure the objectivity of the assessment process. The committees may vary from various organizations; 3) An evaluation of the effectiveness of the criteria for Internal Quality Assurance of the Thai Senate Standing Committee reveals four main key components namely; 3.1) Strategic plan 3.2) Scrutinizing the laws 3.3) controlling the Government Administration, and 3.4) administration and management. There were 20 indicators and 165 items of standard criteria. After testing, it was found that the criteria for effectiveness demonstrated high discriminant validity feasibility.
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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 | 0.074 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".