Political Geographies of Migration: The Borders, from Static Lines to Mobile Entities
Bibliographic record
Abstract
The article explores the border literature in political geography in order to understand the contemporary proliferation of bordering practices in the Western world. It takes the case of President Trump administration’s policies to show how borders can be concealed in social and political practices inside of sovereign territory. This expansion of geographical borders continually shapes the socio-spatial identities of migrants. The text also analyzes why the traditional bordering practice of building border walls is still an appealing resource aiming at keeping immigrants away from Western territories, even after the promise of a “borderless world” in the late 20th century. This article argues that the expansion of border walls is explained by the analysis of three factors: the transformations on the refugee protection framework after the 90s, the change in states’ perception of refugees as a threat to Western societies, and the fear of states to be perceived as actors non-capable to maintain their sovereignty. These contemporary practices are consistent with recent debates in border theory that see the border as a mobile entity instead of a static territorial line separating two units of land. This article aims at fostering the idea of border studies as a way to unveil new forms of power and control. It also pretends to foster an understanding of the interconnectedness of border practices around the world.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".