Lessons Learned from the Resettlement of Yazidi Refugees in Calgary: A Community- Based Assessment of the Second Year of Resettlement
Bibliographic record
Abstract
It is nearly the end of two years since the first Yazidi refugees arrived in Calgary under the Survivors of Daesh Program in 2017. They were resettled by CCIS - the sole official resettlement agency responsible for the group in Calgary. This presentation is based on a collaborative research and assessment project between CCIS and University of Calgary sociologist and her research team. The research tool developed collaboratively is a qualitative community-based assessment interview guide. The tool is designed to explore through in-depth interviews with all Yazidi families in Calgary (about 265 people; 51 families), the successes and the challenges associated with the various settlement metrics in the second year of the resettlement process. This population-level assessment brings together the perspectives of the participants with those of the practitioners’ and merges them with academic’s analytical insights. This proposed presentation is about what we will use from this research to inform settlement services for the future of the current cohorts of Yazidi refugees. An evaluation of the successes and the challenges in services that have been provided to the Yazidi community are highlighted and recommendations are offered which can be used in the future resettlement of the same or similar groups of refugees. The presentation will specifically look into the challenges and success of the main aspects of Yazidi resettlement program including language acquisition, physical and mental health, education for children, housing, finances, family reunification, and the host program.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| 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".