Shadow regionalism and border policing in the political economy of North American integration
Bibliographic record
Abstract
This chapter conceptualizes transnational migration flows across North America as enacting a kind of “shadow regionalism,” one that is integral to the overall political economy of regional integration in North America but that is enacted by migrants themselves through their own transnational patterns of movement, labor, remittance payments, and household social reproduction. At the same time, an increasingly sophisticated project of border and immigration policing has expanded from the US–Mexico border to the US–Canada border and across Mexican and Central American territory with the aim of containing and deterring these migration flows. This policing apparatus profoundly shapes the everyday conditions under which millions of people live, love, and work across North America, impacting the kind of waged income to which they have access; destabilizing short-term economic security; and undermining long-term economic opportunity. The chapter concludes by considering how such outcomes replicate important aspects of earlier twentieth-century structures of formal discrimination and labor control that proliferated across the United States, which can now be understood to have been reorganized at a continental scale. Appreciation of this dynamic affords a critical basis for re-evaluating the character of regional integration in North America and in other global contexts, such as the project of the European Union - where economic integration is also coupled with an increasingly aggressive regime of border and immigration policing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".