Intensity and uncertainty: Performing border conflicts at the US–Mexico borderlands
Bibliographic record
Abstract
Abstract This article draws on border studies that recognise rebordering practices as ongoing performances of conflict between various actors including state authorities, border security agents, migrants, migrant supporters, smugglers, international organisations, lawyers, advocates and others. We draw attention tovariable levels of intensitywith which these conflicts are performed and the impact they have on migrants' ability to exercise their agency. We understand intensity to mean not merely the emotional discursive environment in which these conflicts unfold, and the pressure tactics used by at least some parties, but, more importantly, the speed of the responses by all actors involved in this border performance. Focusing on rebordering practices at the US–Mexico borderlands in 2018 and 2019 adopted in response to new forms of mobility, we characterise these years as a period of high intensity, when rapidly changing policies provoked immediate responses by migrants, and equally speedy counter‐responses by other actors, particularly the US and Mexican administration. We suggest that the volatile architecture of border control in the US–Mexico borders has rendered many strategies employed by Central American migrants to overcome obstacles and create innovative solutions virtually ineffective. The article is based on an ethnographic study carried out between early May and mid‐August 2019 in Mexico.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".