A Comparative Study of Teacher's Empowerment Systems Based on in-Service Trainings
Bibliographic record
Abstract
PurposeThe purpose of this study was to compare teacher’s empowerment systems based on in-service trainings.Methodology The present study was practical in terms of purpose and qualitative research in terms of data collection using a four-stage approach introduced by George. Z. Brady and John Stuart Mill’s method of agreement .The statistical population included countries (Canada, South Korea, Japan, Finland, and Australia, due to the absence of African countries in the ranking, Malaysia due to its close cultural and political context) according to the ranking in Legatum Success Index (2019) and the characteristics of pre-service and in-service teacher training (Arthur Donald chaker and Richard Hines, 1997) that was selected based on purposive sampling .The information needed to answer the questions of this research has been collected through library documents, research reports, encyclopedias and university statutes, and site searches.Findings Research findings show that teacher’s professional development should be considered as a process system and their professional development should be systematically designed, supported, budgeted and reviewed. This system should promote and make teachers effective.Conclusion Each country’s principals and officials considering regional requirements along with upstream documents implement different measures and programs to improve the quality of teacher’s empowerment programs
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".