The Underside of Order: Race in the Constitution of International Order
Bibliographic record
Abstract
ABSTRACT While there is increasing recognition of the role of race in shaping global politics, the extent to which the construction and operation of international order is entangled with race remains underexplored. In this article, I argue for the centrality of race and racialization in understanding the constitution of international order by theorizing the constitutive connections between race and international order and showing how the two can be examined as intertwined. I do this, first, by articulating conceptualizations of both international order and race that center on processes of regulation and regularization. Second, I bring these together to suggest that race be understood as a form of order that functions to reproduce a historically emergent form of hierarchy and domination across a range of spaces and contexts. Third, I operationalize these conceptualizations by outlining and historicizing some of the key features of this racialized and racializing international order, specifically coloniality, the racial state, and racial capitalism, and thereby illustrate important aspects of the persistence of this order. Centering race in the study of international order, I suggest, helps us better understand how racializing hierarchies and racialized inequalities persist in the present and are reproduced through structures and practices of international order.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.047 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".