Globally cross-examining case studies of refugee resettlement: How do these help us understand “successful” refugee integration? A look at refugees and how they are resettled in the USA, Canada, and Italy.
Bibliographic record
Abstract
The world’s refugee crisis is ongoing and increasing in severity. By employing a thematic analysis this thesis seeks to center refugee perspectives to define success in resettlement. Through comparative analysis, commonalities and differences are found within the resettlement practices and policies of the USA, Italy, and Canada. The Integrated Threat and Cultural Adaptation theories are utilized to help understand the actions of both the receiving society and the refugees themselves. Canada remains a top destination for refugees; however, the worsening housing crisis is proving to put significant hardship on refugees. The US refugee resettlement program is contradictory in that it prioritizes accepting the most vulnerable refugees while expecting them to be work-ready upon arrival. Further, the US provides an insufficient amount of assistance (in regard to duration) during resettlement. The Italian Humanitarian Corridors Program is a groundbreaking initiative that proves successful but presents conflicts when trying to expand. Drawing on qualitative and quantitative data from resettlement case studies in each country, the analysis reveals that consistent gaps remain in addressing systemic barriers and personal needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".