Study of Core Competency Elements and Factors Affecting Performance Efficiency of Government Teachers in Northeastern Thailand
Bibliographic record
Abstract
The research aimed to investigate the core competency elements and the factors affecting the performance efficiency of the civil service teachers in the northeastern region, Thailand. The research procedure consisted of two steps. In the first step, the data were collected using a questionnaire with the reliability (Cronbach's Alpha) of .90. The sample size was specified based on Krejcie and Morgan’s table and 352 samples were selected using the simple random sampling technique. The second step involved an in-depth interview of 10 specialists to gain the ways to develop effective performance competency and the statistics used for data analysis included percentage, standard deviation, and descriptive analysis. The questionnaire results revealed that there were 11 elements of 4 core competencies. There were 2 elements for the achievement motivation competency, 3 elements for the service mind competency, 3 elements for the team work competency, and 3 elements for the self-development competency. For appropriate ways to develop core competencies for effective performance of civil service teachers, there were 13 ways which included understanding of related environment, supporting of working resources, having effective management system, reducing losses at work, creating good working atmosphere, promoting participation in decision making, supporting staff for professional development, communicating openly within the organizations, listening to the opinions of others, developing of quality system in work, creating good human relations among staff, building morale in the workplace, and specifying appropriate working patterns.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".