Metropolis British Columbia Working Paper Series. Number 10-12
Bibliographic record
Abstract
Ideally, the Canadian government, researchers, and/or service providers would trace the settlement outcomes of government assisted refugees (GARs) from various countries over time, but such data is expensive to collect and challenging at the national scale. In a modest effort to fill this gap, research with GARs from Aceh, Indonesia was conducted in 2005 (one year after most arrived) and again in 2009 to ascertain settlement outcomes in the areas of housing, official language acquisition, employment, and participation in Canadian society. While the 2009 findings are but a snapshot of social and economic relations among the Acehnese at the time, they offer the fullest available picture of how these GARs are doing; what their concerns, priorities, and challenges are; and what Canadian policies do to facilitate or hinder their aims as new Canadians and permanent residents. More than five years after their arrival, a number of official language and employment issues persist. Spousal sponsorship has proven a salient goal for the majority of men who are still single. Working towards, saving for, and waiting for such relationships to materialize may well be impeding integration aims in Canada. Recommendations to address these situations are offered.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.122 | 0.037 |
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".