Francophone Immigrant Integration and Neoliberal Governance: The Paradoxical Role of Community Organizations
Bibliographic record
Abstract
Francophone immigration is prioritized within Canadian immigration policy, with community organizations receiving government funding to support the integration of French-speaking immigrants. These organizations serve as intermediaries between governmental social policies and individual immigrants, brokering immigrants' occupational possibilities by offering specific services and emphasizing some occupations over others. As part of a critical ethnography, government documents were critically reviewed and in-depth interviews were conducted with six representatives from governmental and community organizations operating within the London, Ontario Francophone minority community. Findings highlight how characteristics of neoliberal governance shape the provision of government services through third party service providers, including community-based non-profit organizations. These organizations currently face neoliberal pressures of decentralization, decreased funding, and increased accountability. Findings specifically address how immigrant integration is constructed in government documents and how respondents viewed the role of their organizations, the particularities organizations face by being embedded within a minority setting, and the challenges this context creates for immigrants. The ways government policies are enacted via organizations have implications for immigrants' occupations. Examining the role of organizations adds an important scale of analysis to considerations of international migration within occupational science, which to date have largely attended to the experiences of individual migrants.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".