FOREIGN AND DOMESTIC EXPERIENCE OF PREPARING FUTURE SOCIAL WORKERS TO WORK WITH PERSONS WHO HAVE SPECIAL EDUCATIONAL NEEDS
Bibliographic record
Abstract
In the article analyzes the systems for preparing social workers to work with people who have special educational needs in universities in the US and Canada. There are revealed features of education in schools of social work at Columbia’s, Michigan’s and other universities. There are indicated experience of Great Britain, France and Germany in preparing future social workers for working with people with special educational needs. In the article considered programs that offer foreign universities in the preparation of future social workers who wish to work with persons with special educational needs. There are described curricula of the higher educational institutions of these countries, which prepare future qualified social workers to work with persons with special educational needs. There are analyzed reasons for the insufficient preparing of future specialists in the social sphere to work with persons with special educational needs. The general features of the process of teaching students of the specialty "social work" in foreign and domestic universities are indicated, among them the continuity of education, multidisciplinarity of training and the diversity of forms of education. The article shows the distinctive features of the professional training of future social workers for working with people with special educational needs, among which emphasis on practical training in foreign universities, the possibility of choosing a narrow specialization in foreign educational institutions and more stringent conditions for admission.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".