Research with Refugee Children and Families
Bibliographic record
Abstract
In the aftermath of the war in Syria, Canada and Germany welcomed thousands of refugees. Scholars in both countries conducted studies to learn how the refugee children and families were faring, and how local populations and nongovernmental agencies were responding. This book presents researchers’ accounts of responsible ethical conduct in complex situations such as these. In this volume edited by Mehrunnisa Ahmad Ali, contributors describe the challenges of data collection, analyses, and dissemination of findings. These include getting institutional and parental permissions to access children; ensuring privacy, comfort, and safety; and developing trusting relationships with those whose language, culture, and lived experiences are very different from one’s own. In doing this work, researchers can get caught between their obligations to the refugee children and families, research ethics boards, service providers, and government agencies. This book also offers advice on navigating these competing ethical obligations. Research with Refugee Children and Families reveals ethical dilemmas, insights, and methodological innovations to build an effective framework for refugee research in various contexts.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".