Colonization, migration, and right-wing extremism: The constitution of embodied life of a dispossessed undocumented immigrant woman
Bibliographic record
Abstract
The aim of this essay is to illuminate the lived experiences of Victoria—an undocumented immigrant woman of Mexican origin working and living in the United States. Drawing on an in-depth interview conducted with Victoria following the election of Donald Trump as president of the United States, we identify a set of discursive and material conditions that inform her lived reality. By examining three mutually constituting stages of Victoria’s life, we invite readers to consider how the imbricated nexus between global manifestations of colonization, migration, and the political rise of right-wing extremism is embodied and negotiated locally by one particular woman. To aid in theoretically informing the excerpts provided by Victoria, we draw on Judith Butler’s recent works in which she develops, individually and collaboratively, ideas of dispossession and precariousness. We find that dispossession and precariousness foreground the currents of vulnerability that are located palpably in Victoria’s narrative. Finally, by engaging with a genre of feminine writing that collapses the traditional boundaries between theory and practice, we revisit the question of praxis in relation to the researchers’ responsibility toward the participants of their study.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.038 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".