The Travel Visa as the Ubiquitous Legal Infrastructure of Everyday Global Mobility Arbitrariness
Bibliographic record
Abstract
Abstract Much work has been produced on the passport and on exceptional regimes of mobility, notably asylum or permanent immigration. There has, by contrast, been less research on that intermediary, ubiquitous legal device that is the visa, a fundamental manifestation of state discretion, a major source of arbitrariness, and a constant assertion of power over moving bodies. The visa is a manifestation of and is embedded in a deeper infrastructure of limited global mobility. Adopting a global and comparative perspective, this Article seeks to analyze the politics of the visa as the indispensable sesame of international travel but also as the ultimate manifestation of that infrastructure’s arbitrariness. The Article highlights an ideal type of the visa especially as it is deployed towards “at risk” categories, emphasizing the sort of performance it requires from applicants in adapting to the system’s demands as they are relayed by officials, corporations and, increasingly, algorithms and artificial intelligence. It will highlight the constraints of applying for the visa, its temporal frames, its assorted conditions, and its denial and possibilities of contestation thereof. The Article contends that, globally, the visa is the key legal infrastructure to channel systemic discrimination, frustrate professional and cultural opportunities, and keep families apart in ways that entrench class and racial divides.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".