The Racialization of the African-American and Asian-American Citizen: A Comparative Legal Analysis
Bibliographic record
Abstract
The perception of racial tensions in North American settler countries has historically been focused on the Black/White relationship, as has much of the theoretical legal discourse surrounding the concept of “race”. Accordingly, the scope of much critical race scholarship has been restricted such that it rarely acknowledges the racial tensions that persist between different racially-excluded minorities. This paper hopes to expand and integrate the examination of Black and Asian-American racialization that critical race scholars have previously revealed. It will do this by historicizing the respective contours of Black and Asian-American racialization processes through legislation and landmark court cases in a neo-colonial context. The defining features of racialization which have culminated in the ultimate divergence of each group’s racialization will be compared and contrasted. This divergence sees the differential labeling of Asian-Americans as the ‘model minority’ while Blacks continue to be subjugated by modern modalities of exclusionary systems of control. The consequences of this divergence in relation to preserving existing racial and social hierarchies will be discussed in the final sections of this paper.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".