Media, Community Building, and Refugee Resettlement Policies: The Impact of Canada’s Welcoming Culture and Media Coverage on the Settlement Outcomes of Resettled Syrian Refugees
Bibliographic record
Abstract
This paper argues that positive online media coverage of Syrian refugees arriving in Canada, and the welcoming culture of Canadian society, have both influenced positive settlement and integration outcomes for Syrian refugees. It also provides a better understanding of Canada’s response to the Syrian refugee crisis and shows how the process of resettlement becomes stronger when local community members and citizens are involved. These arguments are demonstrated firstly by analyzing the relationship between welcoming cultures, positive media coverage, and the perception of refugees. Secondly, the role of media coverage in influencing welcoming cultures in Canada, as well as its role in encouraging community members and ordinary citizens to be involved in national humanitarian projects, is described. Finally, information related to Canada’s welcoming culture and positive media coverage are discussed relative to settlement outcomes, which portrays the strong influence of storytelling and inclusive communities on the success of new immigrants as they rebuild their lives in a new country. The various refugee resettlement programs in Canada are also outlined. The Canadian response to the Syrian refugee crisis has demonstrated to the world a different approach to civic engagement and humanitarian work. This national humanitarian response may be perceived as a major successful project. Nevertheless, it also leaves us with many unanswered questions around the topic, and most importantly, questions about the relationship between politics and power, citizenship, culture, online media and public opinions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".