Methodological Guidelines for the Deontological Adaptation of Future Teachers in the Education Process
Bibliographic record
Abstract
The methodological guidelines of deontological adaptation of future teachers in the education process are considered: the main vectors of adaptation processes to learning, the mechanisms of professional semantic generation, and adaptation algorithms. This article presents the research of a pedagogical experience of teacher training on the subject of Deontology adaptation, a curricular unit which is part of the education degrees taught at Eurasian National University named after Gumilev (Kazakhstan). The foundation of the curricular unit and its characteristics are presented, as well as the analysis of the students' evaluation of its teaching effects as perceived by them. The data analysis, based on some contents of a portfolio, shows a considerable positive perception of those effects. The purpose of the research is to determine and substantiate the study's methodology and develop the organizational and methodological support for the deontological training of bachelor's education as the basis for the formation of their deontological competence and personal and professional development.
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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 | 0.559 | 0.666 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".