Refugee Resettlement and Perceptions of Insecurity: A Comparative Study of The United States and Canada
Bibliographic record
Abstract
In the United States and Canada, refugee resettlement has been the subject of extensive scrutiny and political debate, particularly since the November 2015 terrorist attacks carried out by the Islamic State of Iraq and Syria (ISIS) against targets in Paris. While public opinion polls have shown increasingly negative attitudes toward refugees, existing survey questionnaires only provide a limited understanding of what shapes these views. As such, this study focuses on two important factors that influence attitude formation toward refugees, pre-existing levels of knowledge and contact with minority groups. Using a comparative case study approach, this research examines how refugee resettlement influences American and Canadian perceptions of insecurity. While most research on refugee issues is conducted in major gateway cities, the study area for this research focuses on adjacent rural state and province with low immigration rates, now experiencing increased numbers of resettled refugees. This study uses a mixed-methods approach to collect data in two sequential phases of fieldwork in both Montana and Saskatchewan. A community survey is first conducted in both areas, followed by in-depth qualitative interviews with key informants to discuss and gain multiple perspectives on the survey results findings. Unique features of the survey questionnaire include a brief quiz to measure general knowledge about refugee issues and a section designed to determine levels of intergroup contact. Data is also supplemented through an analysis of documents in both study area locations. This new in-depth research on public perceptions offers a clearer picture of what influences positive and negative attitudes toward refugee resettlement and can help government officials better respond to the concerns of their constituents.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".