Can Italian-Canadians have their cannoli and eat it too? Representations of Race and Italian-ness in Canada's Printed Media
Bibliographic record
Abstract
In 2009, Dina Pugliese, co-host of a popular daily television show in Toronto, stated in an interview that she was hesitant to pursue an on-camera career because she worried that she was too spicy-Italian. Her words speak to long-standing stereotypes of Italians, developed out of eighteenth and nineteenth century representations of Italy. Clearly, hers is not an identity that has been uncomplicatedly subsumed into Whiteness. In this dissertation, I examine the concept of Italian-ness in two culturally specific Italian-Canadian magazines, Panoram Italia and Accenti. Thematic data was collected to explore how the magazines construct the image of the Italian-Canadian in their editorial discourses and how this discourse analysis may serve to reveal existing racialized power relations. I identify parallels between Italy as Europes south and the Italian-Canadian community, and the ways they serve to function as a filter to understanding Italian-Canadian migration and ongoing concepts of difference within Canada. \nThe empirical chapters, based on my discourse analysis of both magazines, focus on the Italianization of John Cabot, Italian-Canadian internment during World War II and the politics of redress, and the space and place of Italian-Canadians within Canadas social and political life. I show that attempts on the part of Italian-Canadian media to legitimize the presence of Italian-Canadians in Canada often result in identities that collude and collide in the construction of Italian-ness as Whiteness. I discuss how critical Whiteness studies, as a theoretical framework, continues to omit differences between White ethnics, particularly in terms of a European continental north-south divide impacting Western thought. By critically analyzing the ways Italian-ness is constructed in printed media, I highlight the general considerations of critical Whiteness studies as obscuring certain identities, which remain peripheral (Satzewich, 2000) within its project, and the paradoxical implications this oversight may have for groups like Italian-Canadians. I note the need to de-universalize the processes of racialization within the construction of Whiteness by unpacking the field from an Anglo-British and Anglo-American dominance. That way, it may anchor the field of Whiteness by acknowledging how localized forms of cultural expression elicit a particular Canadian and Italian understanding of race and belonging.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.019 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".