Research on Consistent Pattern of Development of the Educators’ Ability to Self-Improvement
Bibliographic record
Abstract
The educators’ ability to self-improvement is considered in the article by means of a special system. It can be represented as a three-level hierarchical model with fuzzy structural relationships. The intermediate level of this model contains components that enable the formation and development of the object of the upper level (educators’ ability to self-improvement): 1) motivational-valuable component; 2) the emotional-volitional component; 3) reflective-evaluative component; 4) cognitive component; 5) organizing component. The lower level of the model contains indicators. They make it possible to evaluate the degree of the selected components’ formation and the stage of the development of the investigated ability as a whole. Each state of the educators' ability to self-improvement is characterized by a tuple. The stage reached by the relevant components is the element of this tuple. According to self-assessments received from 214 lecturers and teachers, the most common states were defined. It has allowed to form a sequence of such states. The transitions between them describe the patterns of development of the educators' ability to self-improvement. The described technique allows us to characterize the development of this ability not only among educators, but also among professional managers, doctors, engineers in the field of electrical engineering and electronics, and others.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".