Open Forum: Factors and Ethical Values that Foster a Sense of Belonging Toward the Host Society: The Case of South Asian Communities in Montreal’s Parc-Extension Neighbourhood (Canada)
Bibliographic record
Abstract
Abstract: Place attachment studies developed scales for measuring the sense of belonging using a range of determinants. However, ethical values are rarely dealt with as such in the literature on belonging. This study’s primary objective was thus to understand and rank the factors that, within an immigrant community whose culture of origin is somewhat different from that of the host society, foster development of a sense of place attachment (neighbourhood, city, state, or country). Then, to grasp the role of ethical determinants in constructing a sense of place attachment, the study’s secondary objective was to see, also by ranking, which of the values present in the host society are perceived by members of immigrant communities as fostering their attachment to it. To attain these objectives, the study interviewed forty adult members of South Asian communities living in a Montreal multiethnic neighbourhood. The results show that interpersonal relations, low crime rate and infrastructures are the most important factors to foster place attachment, while fraternity, equality and safety are the most important ethical values.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".