Türkiye Almanya Çin Kanada ve Singapur Öğretmenlik Meslek Kanunlarının Karşılaştırmalı Analizi
Bibliographic record
Abstract
The teaching profession is a field of professionalism that encompasses versatile competencies and qualifications. The long-awaited teaching profession law in Turkey entered into force as of February 2022. In this study, it is aimed to compare the teaching profession laws of countries such as Germany, China, Canada and Singapore, which are shown as examples in different fields, with Turkey’s teaching profession law, which has only recently entered into force. In this study it is determined to reveal teaching profession laws common and different aspects. The data of the research were collected by the document analysis method. The results of the study were created by comparatively examining the teaching profession law prepared in Turkey and the professional laws in other countries. Three themes were revealed examining from teaching profession laws as the aim and scope, teaching profession and teaching career. In the theme of the teaching profession; seven codes were created. The codes were named as the definition of the teaching profession, the qualifications of teachers, the duties of teachers, the rights of teachers, penalties and sanctions against teachers, working time and holidays and rewards. And the last one is the teaching career theme. Teaching career theme consists of six codes as teachers' selection, wage policy, transfer (appointment), in-service training, promotion and retirement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".