Інтеркультурна модель як інноваційний чинник розвитку міжкультурної інтеграції міста
Bibliographic record
Abstract
У статті йдеться про те, що Рада Європи разом із групою пілотних міст запустила амбітну ініціативу щодо\nрозвитку підходу до інтеграції різних спільнот, що стосується дефіциту згуртованості, та пропонує новий шлях\n– програму «Інтеркультурні міста». У ході дослідження виявлено, що міста можуть отримати величезну\nкористь із різноманітних навичок, підприємництва й креативності, пов’язаних із розмаїттям, тим самим\nполегшуючи міжкультурну взаємодію та співтворчість. Особливу увагу приділено розробці інтеркультурної\nмоделі полікультурного міста як успішного чинника міжкультурної інтеграції його соціокультурного простору. One of the most important human need – a need for belonging and identity\nof the community, regardless of language, origin, religion and other differences.\nIn practice, this means recognizing the importance of different cultures and their right to participate in the creation\nof a common identity, which is defined by diversity, pluralism and respect for human rights and fundamental freedoms.\nIn the article, the authors draw attention to the fact that to be successful, intercultural integration model must operate at\na strategic level. Currently, more than 60 cities all over Europe used this model (members of the European and national\nnetworks), including Ukraine, as well as Mexico City, Montreal, city of Japan and South Korea. The model considers\nthe integration is not as dealing with the needs of people who need help to act accordingly, but as a process in which\nsocial and economic institutions able to determine and increase the use of the skills and talents of all and give everyone\nthe opportunity to become productive members of society.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".