(De)bordering Whiteness: Applying Border Theory to Irvin Painter’s <i>The History of White People</i>
Bibliographic record
Abstract
This article explores the development of whiteness in the United States through the lens of border theory, arguing that whiteness functions as a bordered social construct subject to historical processes of (de)bordering. Using Nell Irvin Painter's The History of White People as a foundational text, the article examines key moments in American history when the boundaries of whiteness expanded to incorporate previously excluded groups. It frames these moments as debordering events, shaped by political, cultural, and geographic forces. The analysis positions whiteness as a hegemonic construct maintained through both inclusion and exclusion. While the study focuses on elite discourses and the experiences of populations that have transitioned into whiteness, it acknowledges the limitations of this approach. Ultimately, the paper seeks to enhance our understanding of whiteness not as a static identity, but as a dynamic, bordered concept continually reshaped by social and historical forces.
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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 | 0.004 |
| 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 | 0.032 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".