Tales of Color and Colonialism: Racial Realism and Settler Colonial Theory
Bibliographic record
Abstract
More than a half-century after the civil rights era, people of color in the United States remain disproportionately impoverished and incarcerated, excluded and vulnerable. Legal remedies rooted in the Constitution's guarantee of equal protection remain elusive. This article argues that the "racial realism" advocated by the late Professor Derrick Bell compels us to look critically at the purposes served by racial hierarchy. By stepping outside the master narrative's depiction of the United States as a "nation of immigrants" with opportunity for all, we can recognize it as a settler state, much like Canada, Australia, and New Zealand. It could not exist without the occupation of Indigenous lands, and those lands could not be rendered profitable without imported labor. Employing settler colonial theory, this article identifies some of the strategies of elimination and/or subordination that have been-and continue to be-used to subjugate Indigenous peoples, Afrodescendants, and migrants of color in order to further settler state goals and maintain a racialized status quo. It suggests that further analysis of these strategies will help us find common ground in the diverse experiences of those deemed Other within the United States, and that exercising our internationally recognized right to self-determination- a primary tool of decolonization-may prove more effective than formal equality in dismantling structural racism.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.044 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| 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".