The “Schengen Turn” as the Reshaping of the French-Italian Border: The Critical Convergence of the Geography of Border Controls and Capitalist Dynamics (1990s-2000s)
Bibliographic record
Abstract
On the 1st of January 1993, border controls at the French-Italian border were officially “abolished” in the framework of the Schengen area. However, the border didn’t disappear; rather, its geography adjusted to the evolution of the capitalist logics of the European market, creating a hierarchized borderzone where “boundaries and scales intersect to fuel further expansion of (…) the frontiers of capital” (Mezzadra and Neilson 2013, 321). The article draws on the fields of critical border studies and radical geography to show the critical convergence of capitalist dynamics and border police practices in the French-Italian border zone. This has been reflected in the production of transport infrastructures enabling high-speed cross-border circulation at the expense of local roads and practices. Moreover, the evolution from “fixed” border control to “random and mobile” identity checks led to the creation of a police targeting system that selects passengers on the basis of a social-racial profiling. After the Schengen agreements, the French-Italian border became a mobile device allowing the accumulation of capital through the selection of “legitimate travelers” and the control of “illegal migrants”, enacting locally the “polarization of mobility” (Bauman 1998) that occured at a global scale at the turn of the twenty-first century.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".