Border work: surveillant assemblages, virtual fences, and tactical counter-media
Bibliographic record
Abstract
The new technologies of bio-informatic border security and remote surveillance that have emerged as key infrastructures of reconfigured mobility regimes depend on various kinds of labor to produce the effect of bordering. The current retrofitting and technological remediation of borders suggests their transformation away from static demarcators of hard territorial boundaries toward much more sophisticated, flexible, and mobile devices of tracking, filtration, and exclusion. Borders require the labor of software developers, designers, engineers, infrastructure builders, border guards, systems experts, and many others who produce the “smart border”; but they also depend on the labor of “data-ready” travelers who produce themselves at the border, as well as the underground labor of those who traffic in informal and illegalized economies across such borders. Bordering increasingly relies on technological forms of mediation that are embedded within hi-tech, military and private corporate logics, but are also resisted by electronic and physical “hacks” or bypassing of informational and infrastructural architectures. In this paper we consider three socio-technological assemblages of the border, and the labor which makes and unmakes them: (1) the interlocking “cyber-mobilities” of contemporary airports including visual technologies for baggage, cargo, and passenger inspection, as well as information technologies for passenger dataveillance, air traffic control, and human resource systems; (2) the development of the Schengen Information System database of the EU, and its implications for wider migrant rights and internal mobility within the EU, as well as radical border media that have attempted to intervene in that border space; and (3) elements of the US–Mexico “smart border” regime known as the Secure Border Initiative Network (2006–2011), and those who have tried to tactically evade, disrupt, or undermine the working of this border.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.044 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".