The effect of talent management factors on teacher’s leadership at the secondary schools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Talent management is one of the roles in human resources management and there has been a long debate about talent management for years. This study aims to identify the relationship between talent management and teacher leadership development. In addition, the study also analyzes the talent management and teacher leadership levels. The data are analyzed using descriptive and inferential statistics. Statistical Package for the Social Sciences Software (SPSS) version 23 and Partial Least Squares Structural (Smart PLS) version 3 are also applied to analyze the data. The survey study involves 473 teachers in Malaysia residential school. The findings reveal that talent management and teacher leadership practices were at high levels. There is a significant positive relationship between talent management and teacher leadership development. The results of the study promote the role of talent management that can lead to positive changes in teacher leadership at schools. It is hoped that through this study various stakeholders such as schools, district education offices and the ministry of education of Malaysia will be able to assist in planning and organizing efforts in order to produce good leaders in future. It is hoped that through this study, various stakeholders such as school, district ed-ucation offices as well as the Ministry of Education will be able to assist in planning and organizing efforts to address the role of teacher leadership to produce highly talented future leaders at schools.
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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 | 0.003 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it