A warm welcome in cold places? Immigrant settlement and integration in northern British Columbia
Bibliographic record
Abstract
Immigrant regionalization initiatives that encourage new immigrants to settle outside of metropolitan centres are increasingly common in Canada and often proposed as an aid to revitalize growth in smaller centres. This thesis considers the potential implications of such initiatives on the settlement experiences of immigrants who move to smaller cities. The research is based on interviews with service providers and immigrants in Northern British Columbia. Immigrant respondents described their experiences settling into the small city of Prince George, and service providers from Prince George, Fort St. John and Terrace reflected on their communities’ ability to welcome newcomers. Results revealed the flexible approaches to settlement that immigrants employed to feel more comfortable in relatively isolated and culturally homogenous cities and towns. Findings also emphasized the pressing need to consider the socio-economic and cultural geographies of the welcoming town or city. Both sets of respondents were also asked to give meaning to the term integration. The results of this query showed that service providers were more able to put meaning to integration than where new immigrants, despite the fact that service providers saw themselves as less active than immigrants in the process of integration. Service providers often approached the term conceptually, and gave definitions bound up with ideologies of multiculturalism, acceptance and tolerance. The usefulness of the term for immigrant respondents was very limited. Similar to the concept of regionalization, integration is an interesting idea that requires more grounded research. This thesis helps explore a new area and challenges some generalizations about immigrant settlement and community identity that are often made about places seemingly far away.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".