Conceptual and Technological Support for Self-assessment of the Cadet Training Effectiveness
Bibliographic record
Abstract
The current trends in professional military education and professional activity of special military school graduates as future officers require a more thorough and detailed approach not only to assessing the development of their professional and general competencies, but also constant monitoring of the quality of curricula and speciality-oriented study guides. This paper provides a brief substantiation for the need to create an internal automated programme adapted to the capabilities of a special military school and designed for self-assessment of the quality of cadets' training and key performance indicators for evaluating the training process. The paper also describes the current developments on this matter, proposes approaches to the solution and offers some recommendations. The present paper also presents the results of an empirical study on evaluating the cadets' satisfaction with the quality of teaching and customers' satisfaction with the degree of readiness of special military school graduates. This study is prepared and published within the framework of the grant research project "Development of a comprehensive methodology for evaluating the quality of education of special military school graduates" by order of the Committee of Science of the Ministry of Education and Science of the Republic of Kazakhstan. Research methods: theoretical analysis, generalisation, comparison, modelling, survey, SPSS data processing, interpretation. Expected results: substantiation of the key performance indicators of the educational activities of the special military school, the results of the survey on two samples.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".