Inside/Outside the Circle: From the Indochinese Designated Class to Contemporary Group Processing
Bibliographic record
Abstract
This article compares the conditions surrounding the creation of Canada’s former Indochinese Designated Class and the contemporary group processing program. Under this program the UNHCR identifies and refers entire groupings of refugees for resettlement in Canada. The article also briefly touches on the selection of Tibetan refugees by Canadian officials in the late 1960s and early 1970s. The Designated Class framework streamlined eligibility for resettlement and allowed government officials to respond to persons not covered by the narrower definition of refugee under the Convention. In a similar fashion, contemporary group processing aims to make resettlement processing efficient by skipping refugee status determinations and using group profiles. The aftermath of the Indochinese resettlement programs and the tightening of eligibility under the Indochinese Designated Class have shaped the creation of group processing. Concerns over “pull factors,” economic migration, and fraud, along with the securitization of migration, have led to a preference for homogenous and self-contained groupings of refugees. The article traces the Canadian government’s preoccupation with visualizing and drawing boundaries around groupings of refugees. While both group processing and the former Indochinese Designated Class aim to make resettlement processing efficient, this objective plays a more prominent role under the former, intersecting with security practices in unique ways and informing the selection of groupings of refugees.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.038 | 0.034 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".