Postracial Resistance: Black Women, Media, and The Uses of Strategic Ambiguity

Silverman, Rachel E.

doi:10.14321/qed.7.2.0131

Cited by 5 publications

(5 citation statements)

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“…In this intervention, we take issue with this contention. Building upon extant scholarship in qualitative textual research (e.g., Ceccarelli, 2001;Eco, 1979;Joseph, 2018) and reception studies (e.g., Fiske, 1989;Hall, 1980), as well as our own long-standing experience in quantitative textual analysis, we argue that the inherent meaning multiplicity of some texts (Boxman-Shabtai, 2020) causes disagreement in classification that is not well-understood as measurement error, but reflects true properties of the classified text. After a brief review of current conceptualizations of disagreement in textual measurement, we introduce a notion of valid disagreement, a form of replicable disagreement that validly captures relevant information about the textual content, and must be distinguished from replication failures that threaten reliability.…”

Section: Introductionmentioning

confidence: 90%

Meaning multiplicity and valid disagreement in textual measurement: A plea for a revised notion of reliability

Baden,

Boxman-Shabtai,

Tenenboim-Weinblatt

et al. 2023

SCM

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Reliabilität, operationalisiert als Übereinstimmung zwischen Codie- rern, ist gemeinhin akzeptiert als eine notwendige Voraussetzung für Validität in der quan- titativen Inhaltsanalyse. Diese Sichtweise beruht auf der Annahme, dass für jedes Vorkom- men einer gemessenen Variable exakt eine korrekte Klassifizierung bestimmt werden kann. Entgegen dieser Annahme argumentieren wir hier, dass divergierende Klassifizierungen auch aufgrund von im Text verankerten Mehrdeutigkeiten entstehen können, welche nicht als Messfehler zu verstehen sind, sondern vielmehr relevante Eigenschaften des analysier- ten Materials widerspiegeln. Entsprechend entwerfen wir einen Begriff valider Nichtüber- einstimmung im Klassifizierungsprozess, welcher es erlaubt zwischen textuell verankerten, replizierbaren Mehrdeutigkeiten und Replikationsfehlern, welche die Reliabilität der Mes- sung unterminieren, zu unterscheiden. Wir unterscheiden drei in der Textanalyse weit ver- breitete Formen von Mehrdeutigkeiten – begründet in Unter-Spezifizierung, Informations- überschuss, sowie der Austauschbarkeit von Klassifizierungsmöglichkeiten – welche valide Nichtübereinstimmung zur Folge haben, aber bislang keine hinreichende Beachtung im inhaltsanalytischen Methodenkanon gefunden haben. Aufgrund einer Diskussion mögli- cher Implikationen dieser Auslassung skizzieren wir geeignete Strategien für den Umgang mit valider Nichtübereinstimmung in der inhaltsanalytischen Forschung.

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Section: Introductionmentioning

confidence: 90%

Meaning multiplicity and valid disagreement in textual measurement: A plea for a revised notion of reliability

Baden,

Boxman-Shabtai,

Tenenboim-Weinblatt

et al. 2023

SCM

View full text Add to dashboard Cite

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“…Tisha Dejmanee University of Technology Sydney Asian American food bloggers constitute an overrepresented racial minority within the predominantly white, middle-class and heteronormative food blogosphere. The whiteness of this digital culture is a function of its allegiance to aspirational and commercialised postfeminist (McRobbie, 2009;Gill, 2007) and postrace (Bonilla-Silva, 2003;Joseph, 2018) discourses, and Asian American participation therein is often understood as 'ethnic entrepreneurialism' in which race is commoditised for the assumed white imagined audience. While the confessional tone and the focus on mundane, gendered experiences across the lifestyle blogosphere has been described as radical for women (Lopez, 2009) and instrumental in generating communities of support for girls and women (e.g.…”

Section: Grappling With the Model Minority Myth: Asian American Food ...mentioning

confidence: 99%

Grappling With the Model Minority Myth: Asian American Food Bloggers’ Activism Through Black Lives Matter on Instagram.

Dejmanee

2023

SPIR

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While Asian American food bloggers constitute an overrepresented racial minority within the predominantly white food blogosphere, food blogs rarely constitute productive spaces for discussing or building community around Asian American experiences even when published by Asian American bloggers. This study addresses the need for further intersectional analyses of food blogs that specifically take race into account as it explores the racial discourses generated by Asian American food bloggers participating in Instagram conversations around #BlackoutTuesday and Black Lives Matter in 2020. I find that #BlackoutTuesday authorised the discussion of race in a way that was not previously visible in the food blogosphere – for instance, Asian American food bloggers used Instagram posts in these conversations to identify with the politicised identity term ‘Asian American’ which, in turn, inspired solidarity and action amongst the Asian American community in support of Black Lives Matter. However, Asian American food bloggers also perpetuated elements of the model minority myth, distancing the microaggressions they had experienced from the ‘more serious’ racism and threats faced by Black Americans rather than acknowledging their joint and precarious structural oppression. This data demonstrates that the racial experiences of Asian American food bloggers continue to be silenced or diffracted, largely due to the ways that Asian American participation in this digital culture is structured through assimilation and racial commodification. Yet, despite these limitations, this conversation has value in contributing to visibility around minority allyship within the Black Lives Matter movement while also challenging the premise of political silence by minority bloggers.

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“…This perspective again fundamentally favors lighter-skinned models whose ethnicities are deemed malleable. These models negotiate complex tensions in online retail content as companies capitalize upon the ambiguity of post-race aesthetics (Joseph, 2018).…”

Section: Visual Analysis Proceduresmentioning

confidence: 99%

Computing colorism: skin tone in online retail imagery

Butkowski

Humphreys

Mall

2022

Visual Communication

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A long legacy of media imagery persistently distorts, stereotypes, and ignores marginalized racial and ethnic groups despite widespread calls to diversify media representations. In particular, fashion and beauty media continue to feature light-skinned models and celebrities over dark-skinned individuals, even lightening dark skin with photo editing to achieve ideals of whiteness and lightness. This practice aligns with colorism, or the privileging of light skin tones for access to economic and social capital. This study examines colorism in a particular genre of digital photography, online retail images, as a problem of visual representation. The novel method of visual computational analysis is used to quantitatively compare how mainstream clothing retail brands represent model skin tones across still and video media modes. The findings suggest that analyzed retailers tended to favor light-skinned models on their websites and that model skin tones in product videos were significantly darker than in product photos. These findings are considered through research on race and technology, photographic manipulation, and media misinformation. Ultimately, the study suggests that visual (in)consistencies can reveal the role of structural biases in shaping media representations. The article also provides a methodological tool for conducting this work.

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Postracial Resistance: Black Women, Media, and The Uses of Strategic Ambiguity

Cited by 5 publications

References 0 publications

Meaning multiplicity and valid disagreement in textual measurement: A plea for a revised notion of reliability

Meaning multiplicity and valid disagreement in textual measurement: A plea for a revised notion of reliability

Grappling With the Model Minority Myth: Asian American Food Bloggers’ Activism Through Black Lives Matter on Instagram.

Computing colorism: skin tone in online retail imagery

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