The Making of a Model Refugee: Refugee Resettlement and Employment Services in Ottawa, ON
Bibliographic record
Abstract
In 2015/2016, Canada accepted and resettled thousands of Syrian refugees fleeing the Syrian civil war. As a country that has for decades participated in refugee resettlement, Canada has an extensive network of settlement services available to facilitate this process. These services are developed and implemented by the Canadian state and various non-governmental organizations. This thesis focuses on Canadian employment services that are meant to facilitate the newcomers’ entrance into the Canadian labour market. Since participation in the labour market is seen as an essential step in the resettlement and integration process, employment services have also become key sites for reshaping newcomers into the desirable, self-reliant citizens. Drawing on fieldwork and semi-structured, in-depth interviews, that focus on the experience of highly professional and educated refugees (some of whom have become service providers themselves), this thesis examines the complex role employment services play in the process of refugee resettlement. To better capture how employment services facilitate this transformation, this thesis will introduce the concept of the “model refugee,” imagined as a financially stable, independent, and socially participatory citizen. By examining the barriers newcomers confront when entering the Canadian labour market, this thesis problematizes the notion that newcomers arrive in Canada destitute and uneducated and instead shows how the Canadian labour market devalues foreign credentials (a process known as ‘deskilling’). Ultimately, my research shows that a crucial aspect of becoming a “model refugee” is demonstrating a willingness to accept downward mobility and deskilling as a cost of socio-economic integration.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.042 | 0.015 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".