The Development of the Masters’ Professional Competence by Means of the Information and Communication Technologies
Bibliographic record
Abstract
Summarizing the results of the theoretical and experimental research confirmed the probability of the leading principles of the general and partial hypotheses, proved the effectiveness of solving the set tasks and made it possible to formulate the conclusions.the pedagogical system of development of professional competence of Masters-translators by the means of information and communication technologies, characterized by functionality, complexity, openness, unity and at the same time comparative independence of the structural components, was modeled. The system covers five subsystems: targeted, conceptual and methodological, content, operational and technological, evaluating and efficient. The experimental verification of the effectiveness of the proposed pedagogical system revealed significant quantitative and qualitative changes: the majority of the students of the experimental groups after their studies were completed acquired a level of the professional competence development above satisfactory, in particular, a noticeable increase of students with an average and high level was recorded. The received results were the consequence of the effective author’s pedagogical system and the model of development of professional competence of Masters-translators by the means of information and communication technologies, and therefore with the help of the created pedagogical conditions and educational and methodical support.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".