Migrant World-Making in Yuri Herrera’s <i>Signs Preceding the End of the World</i> and Luis Alberto Urrea’s <i>Into the Beautiful North</i>
Bibliographic record
Abstract
This article analyzes the creation of migrant worlds in two novels set on the US-Mexico border: Yuri Herrera’s Signs Preceding the End of the World (2015 [2009]) and Luis Alberto Urrea’s Into the Beautiful North (2009). Herrera’s world is allegorical, narrating a journey from an unnamed town to an unnamed North, paralleling the stages of the Mayan underworld. Short, mysterious, and enigmatic, his novel gestures towards the ravages of colonization, narco-trafficking and migrant shadow life. Urrea’s book bursts with humour, chaos, fantasy and romance, appropriating the plot of John Sturges’ Hollywood Western The Magnificent Seven (Citation1960) to narrate a mission to recruit American migrants to defend their Sinaloan village from ‘banditos’. Both novels feature fearless young female protagonists on quest narratives to find lost family members. Both imagine worlds of darkness and light, hardship and loss, but also point towards a way to live with dignity and self-respect in the ‘in-between’. They highlight the unacknowledged ‘costs’ of Mexican mobility in imaginative and affective terms, foregrounding the sorrow of those who leave and those who remain. In so doing, they illustrate the imaginative power of fiction to legitimize, humanize, and complexify borderlands and the migrant experience in ways that challenge and provoke existing discourses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".