People‐Processing Capacity: The Origins and Development of Institutions to Render Forced Migrants as Cases in Canada and Sweden
Bibliographic record
Abstract
ABSTRACT States have invested heavily in controlling forced migration for decades, with mixed results. Research often focuses on deterrence, leading to a neglect of bureaucratic boundaries within borders. This article unpacks the unrecognized importance of people‐processing capacity: a state's ability to render forced migrants legible by categorizing them as cases and selecting those perceived as desirable victims. Due to the heterogeneous nature of claims and the agency of migrants, rendering migrants as cases is a deeply complicated process. Using a historical‐institutionalist framework, the article explores the role and historical development of people‐processing capacity through a comparison of the evolution of modern migration control in Canada and Sweden, two states with similar trajectories of capacity‐building but different guiding ideas for migration policy. The results trace the institutional roots of deservingness, reveal different ideals of vulnerable and adaptable refugees, and theorize how persistent governance problems emerge from classification systems intended to order migration.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".