Illegal Skin, White Mask: A Critical Phenomenology of Irregular Child Migrants and the Maintenance of Whiteness in the United States
Bibliographic record
Abstract
In this paper, I reinterpret the experiences and perception of child migrants through the lens of racialization and White Supremacy. I do this by advancing work by Cheryl Harris and Lisa Guenther on the critical phenomenology of “Whiteness as Property” (WaP) and the protection of “White Space.” I build on this foundation by examining the way WaP regulates sociogenic and emotive states in order to protect its accrued resources, resulting in an “economy” of racial identity where ownership produces and is produced by particular societal structures and relationships. I use these concepts in order to understand the framework that willfully misinterprets racialized children. I establish the Child as a sociogenic concept and symbol of national futurity and universalism, and therefore of the futurity and universalism of Whiteness; reiterating and interrogating the inconsistency that many immigration and child activists point to, that there is no such thing as an “illegal” or racialized child. Thus, the irregular child migrants (ICM) either loses the privileges and protections afforded to children or must dawn the White Mask through a performance of victimhood. Through this framework, I undertake an examination of the ICMs as portrayed in the legal process using tools from legal sensorial studies and critical phenomenology, demonstrating the sociogenic shifts that occur for the ICM and how these shifts work to protect WaP.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.030 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| 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".