The Problem of Tutor Profession Institutionalization in the Ukrainian Educational Space
Bibliographic record
Abstract
The modern information society is characterized by such signs as the circulation of large volumes of various information kinds (text, graphics, video) from various sources; the existence of poorly structured, contradictory, rapidly changing information. Nowadays, it is difficult for a person to navigate through large streams of information for learning it. Education, which should help a person in this learning, remains conservative and upgrades slowly. Under such conditions, there is a need for a fundamentally new position in vocational education, a position of a tutor. A tutor is understood as a teacher in the process of the individualization principle makes an individual educational program, taking into account interests and aptitudes of the tutor, provides support in its mastering. Introduction of the profilisation of schools and alternative subjects for upper grades will update the tutor position as a position in the regular school manning table. An analysis of the profession classifiers in Great Britain, Poland, the USA, Canada and Russia testifies the presence of the tutor profession. In the National Classifier of Ukraine "Classifier of professions DK 003: 2010" the tutor profession is absent. The article outlines the difference between a tutor and a class teacher, a subject teacher, a social teacher, a school psychologist, an additional education, an assistant and an assistant teacher. The expediency of introduction of the tutor profession into the list of professions in the National Classifier of Ukraine is substantiated. Functional duties of a tutor in the field of secondary education, a tutor in the sphere of higher education and a tutor in the field of additional educational services are provided. Knowledge that a tutor should have for the execution of the following functions is outlined.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".