‘Perfect Vision’: An Examination of the Role of Census and Profiling Practices in Visualizing and Crafting Refugee ‘Groups’ under Contemporary Group-resettlement Programmes
Bibliographic record
Abstract
Abstract This article demonstrates how the characteristically visual practices of boundary-making around prospective refugee groups comprise an important and instrumentalized version of what Rogers Brubaker (2004) calls ‘groupism’—the assumption that ‘discrete, sharply differentiated, internally homogeneous and externally bounded groups’ are the ‘basic constituents of social life’ (2004: 8). Unlike individual resettlement, group-resettlement schemes (known as ‘Group Processing’ in Canada, ‘Priority-2 group referrals’ in the United States and the ‘Group Methodology’ at the United Nations High Commissioner for Refugees (UNHCR)) involve the resettlement of entire refugee groups. Preoccupations with security and the possibility of identity fraud in these programmes have led to a preference for what are perceived as easily identifiable, finite and homogenous refugee groups. Census and profiling practices permit authorities to visualize and draw boundaries around these types of groups. These practices are the preconditions for the writing of specific narratives of risk, persecution and flight in UNHCR group profiles. An examination of group resettlement reveals how officials do not just choose between pre-existing refugee groups based on racial, national and ethnic categories, but rather attempt to construct an idealized conception of groups reflected in Brubaker’s notion of groupism.
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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 | 0.033 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".