Resettlement of Refugees through the Lens of the Critical Race Theory
Bibliographic record
Abstract
This presentation will showcase my ongoing Master of Laws (LLM) research-based thesis. It explains who refugees are and the processes involved in resettling refugees to different countries outside their countries of refuge. Canada is a big player in the resettlement of refugees. This means that we are individually likely to know some refugees or be in contact with some of them in our various local communities. My research explores resettlement of refugees through the lens of the Critical Race Theory. There has been substantial research focused on various aspects of the resettlement process and ancillary procedures yielding a lot of insight into different aspects of the demographics of resettled refugees to various third countries. It is pertinent to note however that the current research is lacking an important element in the discourse. Addressing this gap is hopefully the intention of my research. It analyzes the historical trends inherent in the statistical data on global resettlement provided by the United Nations Refugee Agency (UNHCR). This research argues that the element of race of refugees has been an underlying consideration significantly affecting which classes of refugees were deemed eligible for resettlement in the past and the quotas that were ascribed to them. In challenging this historical and current trend, this research hopes to get everyone involved in challenging their inherent racial biases in their respective societal spaces in any small way possible to foster inclusivity in diversity. For within this student population will be practitioners and policy makers in the near future.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.045 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".