The Categorized and Invisible: The Effects of the ‘Border’ on Women Migrant Transit Flows in Mexico
Bibliographic record
Abstract
In an increasingly globalized world, border control is continuously changing. Nation-states grapple with ‘migration management’ and maintain secure borders against ‘illegal’ flows. In Mexico, borders are elusive; internal and external security is blurred, and policies create legal categories of people whether it is a ‘trusted’ tourist or an ‘unauthorized’ migrant. For the ‘unauthorized’ Central American woman migrant trying to achieve safe passage to the United States (U.S.), the ‘border’ is no longer only a physical line to be crossed but a category placed on an individual body, which exists throughout her migration journey producing vulnerability as soon as the Mexico–Guatemala boundary is crossed. Based on policy analysis and fieldwork, this article argues that rather than protecting ‘unauthorized’ migrants, which the Mexican government narrative claims to do, border policies imposed by the state legally categorize female bodies in clandestine terms and construct violent relationships. This embodied illegality creates forced invisibility, further marginalizing women with respect to finding work, and experiences of sexual violence and abuses by migration actors. The analysis focuses on three areas: the changing definition of ‘borders’; the effects of categorization and multiple vulnerabilities on Central American women; and the dangers caused by forced invisibility.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| 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".