Effective Improvement of Talents Management for Continuing of Managing Government
Bibliographic record
Abstract
The Talent Management System or the HiPPS (High Performance and Potential System) was developed by the Office of the Civil Service Commission (OCSC) for 10 years since 2003 in Thailand. The purposes of this mixed methods research were 1) to develop of the causal relationship models of the effective talent management, 2) to investigate the percent of predict in each group, and 3) to study the current situation and to investigate strategies to improve the talent management to ensure continuity in government administration. The data were collected from five rating-scale questionnaires which the total -item correlation had been .2-.7. and ? is .835-.909. The sample size were 109 talents, 96 coaches, and 100 human resource (HR) staffs who are responsible for the HiPPS system. Data was analyzed by LISREL and content analysis from 9-19 key persons in each 5 group such as executives, coaches, talents, HR staffs and former talents groups. The results indicated that 1) the causal relationship model of effective talent management for all groups were consistent with an empirical data at a strong level, 2) the person and work environment factors had positive effect and could be predicted in all groups regarding effective talent management for 48 percent in the talent group, 83 percent in the coaching group and 66 percent in the HR staff group, 3) the person and work environmental factors that influence the effective talent management, found that there are seven factors that impact in a positive way and two factors, such as positive attitudes toward the talent management, and organization commitment were a major factor that impacted directly positive influence on effective talent management, and 4) the problems of the effective talent management were 4.1) the executives didn’t understand HiPPS or often change their executives, 4.2) the talents could not rotate as individual development plan and low effective coach system, 4.3) the talent development plan didn’t clear and not support from organization. So, strategies of effective improvement should organize for administrative procedure as talent identifying, talent development, talent monitoring and evaluating, talent rewarding and performance management to share knowledge and innovation for continuing of public sector development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".