Research on the Integration of Chinese Immigrants in Turin: A Case Study of Bar Francesca
Bibliographic record
Abstract
Integration of immigrants, particularly their social integration, is a core keyword in the studies in Italy on immigration nowadays. Negative conducts of Chinese immigrants in Italy in their social integration are in stark contrast to their economic success. This contrast has long been criticized by Italian authorities and academic community. From January to May 2016, the author undertook a case study on the integration issues of Bar Francesca, which was run by a Chinese family in Turin and on the basis of the survey the author wrote this article. From the perspective of a family and a small bar, this paper tries to achieve an understanding of the integration of Chinese immigrants and interpret the successful interaction between Chinese in Turin and the local community. This article studies the challenges and countermeasures in the integration of Chinese immigrants, especially in the process of social integration and it provides references and suggestions for the benign interaction of Chinese immigrants with the local society and the integration of Chinese community in Turin and even in Italy. It also calls on a wider participation of both official and academic circles of China and Italy to provide assistance and incentives for the full integration of Chinese immigrants so that this group can play a more active and important role in the new era of Sino-Italian relations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".