Whiteface as rhetorical metis in Sharmila Sen’s Not quite not white : and, Code meshing: practices for writing space in post-secondary education
Bibliographic record
Abstract
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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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.023 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".