The effect of talent management factors on teacher’s leadership at the secondary schools
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 education 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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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".