Data Supremacy: Race In-Formation Through Herman Hollerith’s Tabulating Machine
Bibliographic record
Abstract
This is an accepted article with a DOI pre-assigned that is not yet published.In this essay, I examine the racialization of data in the United States through the Tabulating Machine, developed by the German American inventor Herman Hollerith in the 1880s to automate census tabulation. Because scientific theories of race at the time posited racial categories to be biologically distinct and hierarchized, the formation of data as natural and neutral followed suit to validate those theories for the hegemonic enterprise of population management. Put simply, distinguishing racial categories through data structures solidified (and continues to solidify) white supremacy. Consequently, methodologies of data formation and deployment center whiteness in the United States. By analyzing how census data constituted notions of race through Hollerith’s machine, I illuminate how a particular racializing discourse, one that prioritizes whiteness, conceives of data. More broadly, I argue that the datafication of race encoded a politics into data itself, reifying specific ideologies into data as well as into an ideology of data. As such, deconstructing data becomes inextricable from deconstructing whiteness.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.041 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".