Bibliographic record
Abstract
This article tells the stories of illegalized migrant people moving through two violent, transcontinental borderscapes: the EurAfrican border that spans Western Europe, the Mediterranean Sea, and pushes further south each year across Africa; and the American border that stretches from the interior of the United States, through Mexico and Central America, and into South America and the Caribbean. Comparative analysis of these borderscapes reveals similar logics, practices, and policies of border enforcement, as well as strategies that migrant people use to subvert them. We argue that fugitivity provides a critical lens for understanding the co-constitution of borders and border transgression, and reveals how the border manufactures its objects—producing fugitive subjects, spaces, and relations across expanding spatial and temporal distances. As a lens rooted in histories of racialized control over human mobility, fugitivity allows us to chart contemporary territorializations of racial domination through bordering alongside constant challenges to these territorializations through movement. Ultimately, fugitivity provides a method that not only maps out the violence and failures of bordering, but one that imagines alternative geographies emanating from the underground of marginalized people, spaces, and relationships.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.039 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".