The e-Walls of Brexit: Digital Adjustments in Customs Programs Between France, Belgium and the United Kingdom
Bibliographic record
Abstract
Contemporary border wall policies are characterized by digitization of control procedures to filter out mobilities and adjust control to trade flows. This digitization has been mostly studied in terms of technology deployment and usages by state and non-state actors to deal with migration. When applied to control of trade flows, “smart bordering” also involves functional adjustments between public and trade actors, as well as customs cooperation. The bordering at stake with Brexit provides a revealing case study. Brexit entailed supply chain security for goods crossing the UK–France/Belgium border. For companies, transporters, and customs, avoiding physical controls at ports became imperative. Each country generalized data sharing and pre-lodgment through “smart bordering” models and various Information Technology (IT) programs. This paper examines such digitization programs and the adjustments between public and private actors confronted with IT programs and the mutation and cooperation of customs agencies in this process. It concludes that the UK lacked anticipation, while France and Belgium were proactive in accordance with EU harmonization. The digitization of control also reveals changes in identity-making of customs, emphasizing its autonomy as a border agent and its role as a trade supporter, on top of traditional policing or taxation duties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".