Money Lightens: Global Regimes of Racialized Class Mobility and Local Visions of the Good Life
Bibliographic record
Abstract
O dinheiro embranquece. Money whitens. Money lightens.T his Brazilian idiom, serving as the title of this issue, reflects a broader Latin American folk belief that once racialized minorities have accumulated sufficient financial capital they can escape stigmatized non-white racial identities in name (e.g., Indígena, moreno, prieto) and in practice, that is, the poor treatment and lack of respect that accompanies non-white status.Money lightens is a provocative turn of phrase that unmasks the mutability of race and this mutability's intrinsic link to capital accumulation.It is also deceptively simple: it claims that all that is needed to uproot and transcend racism is money.Of course, the inverse is also true: a lack of money can further entrench racism's hold.Money lightens, is a belief in a particular kind of mobility, one where accumulation of money trumps race.Drawing from this conceptualization, we define mobility as the uneven processes whereby lower-status individuals and their families attempt to ascend hierarchies of social stratification, access additional material resources and comforts, and enjoy a meaningful change in their social status.Furthermore, mobility is defined by the structural violences that disable it.Chief among these violences is racism.We examine the ways mobility and race intersect, demonstrating how our interlocutors make sense of their chances for mobility as constrained by racialization and how they critique racial orders of inequality as they attempt to get ahead and forge a good life.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".