The appification of borders: Data, migration and digitalization
Bibliographic record
Abstract
The intersection of migration, borders, and technology has been extensively studied in critical security studies, science and technology studies, law, and beyond. This article argues for closer attention to smartphone and other apps in the growing focus on the datafication and digitalization of borders. In recent years, states have increasingly made use of apps for customs declarations, visa and residence permit applications, and even claims for asylum. Such technologies are at the core of a tension between facilitation and fast mobility on one side, and the intensive need for data and prediction on the other. We contribute to the literature on datafication of borders and describe the ongoing ‘appification’ of the border in relation to three key logics. The first is the interoperation of the technical and bureaucratic infrastructures of the border and of consumer technology, in which apps are software products dependent on consumer hardware and platforms, as well as technologies of sovereign power. The second is a logic of efficiency, through which apps allow the state to more efficiently target and profile travellers as well as make time and cost savings for a range of stakeholders such as airports and airlines. The third is individualization, with apps benefiting from the wide use of personal devices and enabling more fine-grained control over mobility. To illustrate these trends, the paper draws on the cases of ArriveCAN (Canada), Customs and Border Protection One (USA), and the International Air Transport Association OneID initiative.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.055 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".