Building a Life Despite It All: Structural Oppression and Resilience of Undocumented Latina Migrants in Central Florida
Bibliographic record
Abstract
Immigrants to the United States encounter a multitude of challenges upon arriving. This is further complicated if migrants arrive without legal status and even more so if these migrants are women. My research engages with Kimberlé Crenshaw’s concept of intersectionality to examine interlocking systems of oppression faced by undocumented migrant women living in Central Florida. I worked mainly in Apopka, Florida, with women who migrated from Mexico, Central America, and South America. I found that three broad identity factors shaped their experiences of life in the U.S.: gender, undocumented status, and Latinx identity. The last factor specifically affected women’s lives through not only their own assertions of their identity, but also outsider projections of interviewees’ race, ethnicity, and culture. My research examines how these identity factors affected my interviewees and limited their access to employment, healthcare, and education. Through a collaborative research project involving work with Central Floridian non-pro t and activist organizations, I conducted interviews and participant observation to answer my research questions. Through my research, I found that undocumented Latina migrants in Central Florida face structural vulnerabilities due to gendered and racist immigration policies and social systems, the oppressive effects of which were only partly mitigated by women’s involvement with community organizations. My research exposes fundamental and systemic failures within U.S. immigration policies and demonstrates that U.S. immigration policy must change to address intersectional oppression faced by undocumented Latina migrants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 | 0.000 |
| 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".