Bibliographic record
Abstract
This chapter deals with Italian-Canadian women writers and the multiple ways in which they tackle the trauma of migration in their works, which become sites of resistance, agency and healing. By focusing mainly on writers who immigrated to Canada as children in the 1950s and 60s (e.g., Mary Di Michele, Caterina Edwards, Genni Gunn, Gianna Patriarca, Dore Michelut, and Licia Canton), it examines how, in their endeavors to renegotiate identity within a transcultural paradigm, they devise various strategies to come to terms with their linguistic and cultural duality. These include multilingual experimentation and self-translation, crypto-ethnic metaphorical transposition, as well as a dialogic and parodic engagement with Italian culture, so as to debunk culture-bound stereotypes about Italian femininity and idealized visions of mothers and the motherland. While documenting the loss and pain caused by the migratory experience and the hardships of settlement in Canada, they add a gender perspective to the Italian-Canadian diaspora which re-inscribes the role of women as both preservers and transmitters of Italian cultural traditions and agents of change. Indeed, their writing enacts a transcultural fusion of old and new cultural elements and elicits a reformulation of ethnic identity as a hybrid, fluid and intersubjective space in which to accommodate plural modes of being and belonging and to reimagine notions of home and nation in transnational and cosmopolitan terms.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.042 | 0.014 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".