Bibliographic record
Abstract
This study looks at the relationship between immigrants' social network structures, their identities, language status, and trust, to determine if the type of social networks they have is related to information access, linguistic capital, social capital and opportunities for L2 communication. Data was collected via an online survey provided in Spanish, English, Chinese and Arabic, sampling immigrants in Québec who speak a language other than French as their first language. Data from the World Values Survey Wave 5 for Canada were used to compare findings from this study to determine if participants differ significantly on measures of trust, identity, social network structure, and level of social capital, and to make links between these indicators and the access they have to information about services for newcomers. Findings indicate that participants tended to have lower levels of social capital than Canadians from the World Values Survey of Canada. Most participants did not tend to identify as Canadian or Québécois regardless of citizenship or nationalization status. Participants with primarily bonding ties in their social networks had less access to federal and provincial sources of information and relied more on their social networks for information. Social networks were shown in this study to be related to the types of information participants were able to access. This may suggest the importance of social network mapping in determining how to disseminate information about services to immigrant communities.
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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 | 0.002 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".