MULTILINGUAL ARABESQUES IN THE NOVEL IN NORTH AMERICA
Bibliographic record
Abstract
ABSTRACT Rachel Anne Norman: Multilingual Arabesques in the Novel in North America (Under the direction of María DeGuzmán) “Multilingual Arabesques” examines the literary and linguistic constructions of identity in the Arab diaspora in North America. Novels, and the languages used to write them, are cardinal spaces of cultural belonging. Arab North Americans’ inclusion (or not) of Arabic in their fiction establishes a linguistic identity that situates characters, texts, and authors within and beyond national spaces. By comparing representations of Arabic as a “foreign” language in novels from Canada, Mexico, and the United States, this dissertation argues that Arab diasporic writers invoke language to perform identity in contextually contingent ways. Within the United States and Canada, Arabs are socially constructed as “enemy,” “other,” and “fanatical terrorist,” and authors claim ethnic and national belonging through representations of code-switching and translingualism that powerfully contest and transform the spatial hegemony of the nation-state. Absent the same historical constructions of race, Mexico figures Arab immigrants as corrupt businessmen out to cheat “real” Mexicans. Arab Mexican authors variously utilize Arabic not as a tool to modify the nation but rather to create a linguistic space that stands outside geography. Chapter 1 explores the form and function of the intersections between language and identity categories like ethnicity, race, nation, class, gender, and sexuality. Continuing the discussion of gender, Chapter 2 argues that an Arab diasporic identity is inscribed within the female body through the cultural resources of food and language, while Chapter 3 suggests that queer Arab American characters inhabiting non-normative narrative structures challenge homonational global politics. Finally, Chapter 4 elucidates how authors manipulate language to normalize the presence of Arabic and Arab bodies by inserting Arabic into the linguistic landscape of North America. Although the Arab linguistic production of identity differs between Canada, Mexico, and the United States, all three Arab immigrant communities enlist language in the rhetorical and material pursuit of belonging. The first study in the field to compare nationally and linguistically diverse Arab diasporic texts, “Multilingual Arabesques” helps us to understand critical points of continuity and rupture within the Arab diaspora in North America.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.026 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".