Making Brown/ness(es): Aesthetics in the Everyday
Bibliographic record
Abstract
Making Brown/ness(es):Aesthetics in the Everyday Naveen Minai, Vanita Reddy (bio), Marissa C. de Baca (bio), Aaisha Salman (bio), Xine Yao (bio), and Mira Al Hussein (bio) culture offers a pleasurable playground for exploring the quotidian sedimentations of meaning termed brown. These sedimentations, crafted through cultural practices, objects, and vocabularies, network the global and the local, the intimate and the infrastructural. This A&Q feature considers what the methodological and conceptual mobilizations of brown/ness(es) might do for us in studying culture. The scholars gathered here take up the following questions: 1. How might foregrounding brown/ness(es) in relation to Global Asias through cultural objects and cultural practices make visible new issues/questions/problems/subjects? 2. How does brown(ness) work as an analytic of the gendered frictions of intimacy, space, labor, and value in the everyday? 3. How does brown(ness) function as a cultural technology of self and space? The scholars included in this feature deal here with multiple fragments of the meaning of brown in their contributions. By orienting our attention toward brown/ness(es) as analytic and the conditions under which brown/ness(es) are produced, they drift away from brown as ontology, synonym, affect, subject, object, or coherent racial marker while simultaneously detailing how these meanings operate. Their contributions show the complex ways in which meanings of brown/ness(es) twist, break, and (re-)align cartographies of value and desire. These meanings are transformed and congealed within national borders as much as across them. Our contributors linger with the incommensurability and untranslatability of these meanings across geopolitical contexts even as race and ethnicity [End Page 25] are global technologies of difference. Moreover, they demonstrate the multilayered ways in which brown as an analytic shows both the fractures under the term community (imagined as diasporic, national, ethnic, racial, or cultural) and the mechanisms deployed to obscure these fractures. Each contribution in this feature turns the volume up, in different combinations, on three shared themes: the aesthetic, the feminine, and the quotidian. Because cultural studies allows us to understand these themes at the nexus of the intimate and the infrastructural, here brown is used to highlight that nexus. Vanita Reddy and Marisa C. de Baca explore fashion as a site of inquiry, focusing on Afro-Asian femme and queer diasporic methods and tools of resistance and challenge to global regimes of value and aspiration. Aaisha Salman takes up aspiration as a key component of how brown is constructed as a gendered fantasy of domesticity in diasporic culinary cultures anchored in class and caste exclusion under American capital. If, in Reddy and Salman's work, brown signifies the diasporic routes of desire and value between the United States and India, then Xine Yao relocates us from Canada to England to offer brown as a name for disorientation and unease about grammars of racial difference and coalition across different colonial regimes and different geographies of shared colonial regimes. Mira Al Hussein continues and deepens this geopolitical disorientation by situating her work in the Persian Gulf, using the politics of shades of brown to discuss complicated gendered metrics of valuation as coordinates of belonging for the Ajam. Reddy, Yao, and Al Hussein all grapple with brown/ness within the histories of race as a globalized technology of binding a/the body into coherence under global racial capital. Their work, alongside Salman's, pushes us to think about the messy racial politics of the feminine. Salman and de Baca's analyses point to labor in domestic and industrial spaces as a key question. Both Yao and Al Hussein illuminate how race as a globalized technology of difference still accrues meaning in different ways in various geopolitical contexts. Crucially, their insistent pausing on unease and discomfort troubles the temptation to universalize the meanings and operations of brown/ness(es). Afro-Asian Femme Aesthetics in Anticolorism Campaigns vanita reddy In this contribution to A&Q, I explore "brown/ness(es)" as naming possibilities for Afro-Asian (femme) political relationality as they emerge [End Page 26] within the critically overlooked domain of fashion and beauty cultures. I examine the soft politics1 of beauty in grassroots diasporic anticolorism campaigns. Colorism...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".