The representation of machismo in literary journalism: How Luis Alberto Urrea, Ruben Martinez, and Mexicans narrate stories of machismo
Bibliographic record
Abstract
This article uses critical discourse on the genre of literary journalism to conceptualize machismo as a primary means of representing the male gender in Mexico. The way gender and machismo are socially constructed and the stories Mexican men tell themselves about machismo influences their performance of it. This article addresses issues of gender representation and discusses what literary techniques authors of literary journalism employ to investigate the construction of Mexican masculinity. Modern day conceptions of machismo are still associated with traditional connotations of hyper-masculinity; it is a socially prescribed role internalized as the public ideal acting to inform women of societal expectations of men. Engrained deep in the culture, machismo is to a degree exacerbated by alcohol, leading to violence and spousal abuse. One major question is whether literary journalism can lead to a greater truth if authors use stylistic techniques that limit the reader’s understanding of how conclusions were formed. However, this question is inconsequential if it can lead people to find their own truths and start social change. Whether the actual connotations of machismo within the Mexican culture are changing is minor compared to whether Mexicans can reach a higher truth by negotiating the representation of gender and machismo in their own lives. How machismo is represented can lead to social change as stories are constantly changing. Keywords: machismo (representations of); male gender (social constructions of); gender representation; Mexico; stylistic techniques (writing); literary journalism
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".