Bibliographic record
Abstract
Note on LanguageThe descendants of Africans in Brazil in the 1920s called themselves and each other a broad array of terms.They used negro, de côr, de classe, preto, pardo, mulato, other color terms, and all the terms for white shades as well, of course-and many refused racial or color identifications at all, sometimes successfully.Historical actors are as inconsistent as contemporary subjects; all of us encounter and use the instability of racial categories.So how should a historian write of such subjects when discussing the impact of race?For historians to use a single term carries elements of coercion, forcing people into categories they resist or exceed, and ironing over bountiful heterogeneity.Yet the use of multiple, inequivalent terms makes it difficult or impossible to recognize the organizing power of racism.Worse, accepting the classification system on the ground in the period studied can strengthen those elements in contemporary ideology that are the legacies of that period.I negotiate between these twin dilemmas with a split decision.In my own writing, I embrace the artifice of anachronistic umbrella terms that highlight rather than conceal that process of coercion and allow, albeit imperfectly, for a discussion of racism.For Brazilian subjects I choose "Afro-Brazilian" and "Afro-descended," the terms emerging from anti-racist activism in Brazil since the late 1970s, avowals of solidarity with Portugal's ex-colonies in Africa and with Afro-diasporic communities worldwide.I use "African American" for Afro-descended North Americans not from Canada (gritting my teeth about the equation of "America" with the United States as the alternative is simply too unwieldy).More happily, I use "Afro-American" for people of African de-
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.226 | 0.135 |
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".