CONCEPT OF TOLERANCE IN THE SYSTEM OF SOCIAL ADAPTATION OF MIGRANTS AS A COMPONENT OF NON-FORMAL EDUCATION (ILLUSTRATED IN THE MODEL OF CANADIAN PROVINCE OF QUEBEC AND FRANCE)
Bibliographic record
Abstract
The article deals with the notion of tolerance as one of the key values of modern democratic society, included in educational programs of universities, informal organizations, private foundations, religious communities promoting tolerance in the framework of non-formal education. The experience of Canada (classical approaches) and France (innovation) in the field of non-formal education is presented. Due to its flexibility, non-formal education plays a major role in the integration of migrants of different age, social and ethnic groups, determining the prospects for employment, social adaptation, personal development and participation in the state’s democratic life. The issue of socio-cultural adaptation of migrants is of great importance in the contemporary world due to such global undesirable processes as religious and ethnic conflicts, cases of racial discrimination. Migration is a natural process in the era of globalization, which, however, necessitates the adaptation of migrants to the host country’s cultural background. While European countries, recently experiencing an influx of migrants, are urgently searching for mechanisms for adapting the previous socio-cultural experience of migrants to the lifestyle, behavioral norms of the host country, Canadian province of Quebec has been efficiently using the tools of formal and non-formal education for over fifty years to build a more humane and tolerant attitude of citizens to each other, to eliminate conflicts and social aggression
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".