Exploring Best Practices and Tensions in Immigrant-Led Community-Based Social Service Planning Models for Immigrant and Refugee Communities
Bibliographic record
Abstract
Canada’s immigrant resettlement model places non-governmental community-based agencies at the front of service delivery, with program funding often provided federally through Immigration, Refugees and Citizenship Canada (IRCC). Historically, “top-down” approaches supporting immigrants have been planned and employed by governments and corporations to institute exclusionary policies and regulations. In contrast, “bottom-up” approaches, including grassroots community-based initiatives, have addressed localized issues in newcomer and refugee experiences. While current resettlement models rely on grassroots and community-based programs to deliver needed services, there are nonetheless few community-based planning models for social services that are led or informed by immigrant community members, including newcomers and refugees. This article explores immigrant-led, community-based social service planning models to inform and strengthen integration support for newcomers and refugees in British Columbia, Canada. Drawing from community-engaged principles, this article presents findings from a secondary analysis conducted by a team of researchers from Simon Fraser University with the collaboration of community partners. Based on the guidance of community partners in the immigrant and refugee settlement sector who are leading the direction of the project, the information presented in this paper focuses on community-based initiatives related to four social determinants of health: housing, employment, gender identity, and immigration status. Findings are based on immigrant integration models from case studies and published research across the globe and elucidate critical themes in terms of promising practices, tensions and challenges, and recommendations for effective service delivery models in immigrant integration organizations.
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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 | 0.036 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.034 | 0.032 |
| Scholarly communication | 0.025 | 0.007 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| 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".