Intra-Latina/Latino encounters: Salvadoran and Mexican struggles and Salvadoran–Mexican subjectivities in Los Angeles
Bibliographic record
Abstract
In the last 30 years, the mass transnational migration of Salvadorans and Mexicans to the U.S. from their countries due to changes in the world capitalist system, and its specific effects on their homelands, has made Los Angeles the most Mexican and Salvadoran-populated city in the United States. Within the everyday struggles of the working class in Los Angeles, an internal antagonism between these two Latina/Latino communities has developed that has divided them yet, dialectically, a sense of solidarity between them vis-à-vis the dominant racialized regime of the U.S. has also emerged. This paper investigates this dialectical interplay of tension and solidarity between Salvadoran and Mexican communities in Los Angeles through qualitative interviews with 20 young adults who are children of mixed Salvadoran–Mexican migrant families. This paper will contextualize their families’ experiences within a larger theoretical, analytical, and historical framework of the global capitalist system and recent transnational processes, including neoliberalism, migration, and the racialization of Latina/Latinos in the U.S. The exploration of the participants’ families and their relationships to a series of structural and cultural factors that ground both communities, such as racialized labor market competition, migration, and national belonging, may assist in explaining this dialectical interplay of tension and solidarity.
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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 | 0.003 |
| 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 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".