‘Bad hombres’ across the border? Experiences of discrimination and exclusion beyond ethno-racial bias among immigrants and return migrants in Mexico
Bibliographic record
Abstract
Latinos in the United States have long been excluded and stigmatised, yet little is known about how Latino immigrants and returnees are perceived in Mexico. Using data from the 2022 Mexican National Survey on Discrimination (ENADIS), this study compares reported discrimination and exclusion among immigrants, first- and 1.5-generation returnees, and second-generation returnees to non-migrant Mexicans. The findings show marked heterogeneity consistent with three overlapping mechanisms: civic exclusion, ethno-racial bias, and stigma attached to migration-specific attributes (e.g., dress, tattoos and accent). Foreign-born immigrants report higher appearance-based discrimination and unfair denial of employment than returnees, even after controlling for skin tone. Second-generation returnees show higher odds of unjustified detention and obstacles to school (re)enrolment, patterns not fully explained by ethno-racial bias or legal exclusion. The results clarify how migration and discrimination intersect in Mexico and frame integration and reintegration as distinct processes shaped by legal barriers, ethno-racial hierarchies and migrant stigma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".