The Latino Diaspora Is a Dystopia: U.S. Chicano and Latinx Experiences of Intersectional Oppression in Fiction - an Introductory Essay and an Original Short Story Collection: Good Burritos Don’t Fall Apart and Other Stories
Bibliographic record
Abstract
The introductory essay of this thesis looks at U.S. Latino and Chicano writers such as Junot Díaz and Sandra Cisneros, their craft, inspiration on my own work, and contribution to Latinx literary fiction. I argue that all fiction reflecting the authentic experiences of Chicanos and Latinx in the United States is, by definition, dystopian.\nI also take a look at a few non-Latinx authors of other oppressed identities, such as U.S. Afro-futurist Octavia Butler, and Canadian Margaret Atwood, whose dystopias describe socially and politically disenfranchised characters as well. Like the Latinx protagonists of my own work, their characters experience multi-layered oppression, including misogyny, systemic racism, economic and educational inequities, political terror, sexual abuse/assault, and other traumas.\nThe only way to guarantee authentic narratives of the lived Latinx/Chicano experience, even fictionalized, is for us to write them ourselves. In that spirit, I have written an original collection of my own socially dystopian short stories, with major and minor Latinx characters of varying intersectional identities. Among them are first-generation college students, immigrants, queer and transgender Latinx, and men from working-class backgrounds. As a whole, these characters paint a diverse portrait of the U.S. Latinx diaspora.\nThe setting of the collection is the current cultural and political landscape, where economic, educational, and social inequities are symptomatic of American policy, systemic racism, and capitalism. The parents of most of these protagonists grew up in Latin-American countries, resulting in fiction that reflects a very real inter-generational dystopian experience.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.018 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".