Evaluating the sense of belonging to the place of Iranian immigrants with an emphasis on physical and environmental factors in the public spaces of the city (with a focus on Iranians living in Toronto, Canada)
Bibliographic record
Abstract
Today, solving the psychological and two-sided crisis of immigrants' lack of sense of belonging to the destination society in the immigration process is essential as a social phenomenon. The present study investigated the physical dimensions of the sense of belonging to urban spaces and its effective indicators in Iranian-populated areas of Toronto, Canada. The type of research is analytical-descriptive and the method is survey. The study population was 100 Iranian immigrants living in the Richmond Hill, Vaughan, Markham, and Thornhill areas of Toronto, Canada, who were selected using the snowball method. Data analysis was performed in SPSS software. The results showed that among the physical factors affecting the sense of belonging, the safety and security component was the most important component in creating a sense of belonging with 32 percent of effectiveness, followed by the environmental component and the accessibility component, and the human scale component had the lowest percentage of effectiveness in creating a sense of belonging to urban spaces for immigrants.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".