Whiteface as rhetorical metis in Sharmila Sen’s Not quite not white : and, Code meshing: practices for writing space in post-secondary education
Notice bibliographique
Résumé
Across her memoir, Not Quite Not White: Losing and Finding Race in America, Sharmila Sen recounts her endeavors to generate a series of embodied rhetorical strategies that enact what she refers to as “whiteface.” I argue that Sen’s decision to wear whiteface is a rhetorical strategy for survival that operates as the Greek concept metis, due to its concealment, responsiveness, and cunning ability to act. The need to survive in a new environment was initiated by her father’s unexpected job loss which propels them to emigrate to the US, therefore enacting exigencies on multiple levels of the family’s everyday life. In her memoir, Sen illustrates the reality of how deeply and racially problematic assimilation is during a time in which the political climate of the US is charged with debates regarding immigration reform and race. With its 2018 publication, I interpret Sen’s memoir—her revelation of whiteface, her appropriation of it, and her need to express her personal and political responsibilities as personal and political exigencies—as her speaking to a larger kairotic moment in the US. As both narrator and rhetor, Sen is conscious of her US audiences and their perceptions of ethnicity and race based on US immigration laws. Keith Grant-Davie’s concept of a compound rhetorical situation supports my contention that Sen’s memoir serves both personal and political kairotic purposes for her, therefore, operating on multiple levels inside and outside the text. AND This position statement aims to debunk myths regarding negative perceptions surrounding the use of multiple dialects in writing spaces, to illustrate how writing instructors may incorporate multidialectal and multilingual pedagogical strategies in US writing spaces, and to expand on traditional English writing instruction. English is already a multidialectal system in which speakers are encompassed in, and many speakers are already multidialectal and multilingual; therefore, a translingual approach via code meshing should be recognized in academic writing as well. Assessment based on solely an American cultural context further excludes speakers of other languages and perpetuates language hierarchy. A code meshing approach seeks to challenge and transform traditional writing practices, address standard language ideology and students’ anxieties about academic writing, and the ways gatekeeping practices can consequently generate bias myths about language. Code meshing in academic writing further offers diverse possibilities for writing teachers and writing center consultants to encourage, strengthen, and advocate for students and their voices on the written page. The writing spaces that I envision this position statement may apply to include post-secondary composition classrooms such as first-year writing or more advanced writing classes, consulting sessions in writing centers, and K-12 writing classrooms. This position statement urges writing instructors, particularly those in post-secondary education, and writing center administrators and practitioners to teach students the rhetorically strategic ways dialects and languages can function in academic writing. The research supporting this document focuses on speakers of Ebonics, Spanish, and English as the primary dialects and languages incorporated in multilingual approaches for writing education.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,017 | 0,023 |
| Communication savante | 0,010 | 0,006 |
| Science ouverte | 0,001 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».