Tanárképzés az adatok tükrében • Teacher Education in the Light of Data
Bibliographic record
Abstract
A tanulmány hazai és nemzetközi adatok alapján mutatja be a hazai elöregedő tanártársadalom kulcskihívásait, illetve a tanárpolitika ebből adódó sürgető feladatait.Magyarországon nemzetközi viszonylatban is kiugróan alacsony a fiatal tanárok aránya, míg magas az életpálya végén levőké.Az elmúlt három évben a tanárok létszáma több mint 20 ezerrel, vagyis a 2019-es létszámhoz képest 18%-kal csökkent.E pályaelhagyás elsősorban a középkorú (30-44 éves) korosztályt érintette.Az adatok szerint a jelenleg nyújtott tanárképzési formák nem biztosítják a megfelelő volumenű tanárutánpótlást az országban.Mindezért a tanulmány a tanárok utánpótlásának tervezéséért felelős központi funkció létrehozását, a pályakezdő tanárok béremelését és a képesítetlen tanárok iskolai alapú, munka melletti képzési rendszerének kidolgozását javasolja. aBSTraCTThe study presents the key challenges of the Hungarian ageing teacher population based on national and international data, and indicates urgent tasks to be addressed by teacher replacement policies.In Hungary, the proportion of young teachers is excessively low, while that of teachers at the end of their career is high.In the last three years, the number of teachers has decreased by 20 thousand, it is 18% lower than in 2019, and primarily, middle aged teachers (between the ages of 30-44) have left the teaching profession.According to the data, the existing teacher education programmes do not produce enough teacher graduates in the country.Based on these trends the study suggests that a principal institution should be established responsible for planning and overseeing teacher supply, regulating salary raise for novice teachers, and developing of a new, school and work-based teacher education form for unqualified teachers.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.098 | 0.036 |
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".