Manifestation of Social Trust among Migrants: The Case of Iranian Residents in Toronto, Canada
Bibliographic record
Abstract
This paper is excerpted from a research project titled “A Sociological Analysis of Socio-economic Situation of Iranian Migrants in Canada (Case Study: Toronto). This survey research has been carried out in 2005. Its main goal is to answer the following questions: What is the trust level among Iranian migrants residing in Toronto? What is the difference between the in-group social trust level (trust among Iranians) and the out-group trust level (confidence toward Canadians living in Toronto)? In an attempt to answer these questions a sample of 182 Iranians were interviewed on the basis of a standardized questionnaire. The findings reveal that the in-group social trust level – as measured against the defined scale – is slightly below the average, whereby the difference from the middle point of the scale is statistically significant. Moreover, it demonstrates that the mean figure for out-group social trust is significantly higher than the average defined on the scale. The results also suggest that the most important cause for lower in-group trust should be sought for in the pre- migration period. Researches carried out on social confidence indicate that weakness of social trust in home country is often transferred to other countries – after migration – and is intensified due to problems of the migrant community and increase of social risk.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".