Italian Mothers and Italian-Canadian Daughters: Using Language to Negotiate the Politics of Gender
Bibliographic record
Abstract
This paper examines how migration redefines family narratives and dynamics. Through a parallel between the mother and the mother tongue, I unravel the emotional, linguistic, social, and ideological connotations of the mother–daughter relationship, which I define as a ‘condensed narrative about origin and identity’. This definition refers to the fact that the daughter’s biological, affective, linguistic, and socio-cultural identity grounds in the mother. The mother–daughter tie also has a gendered dimension, which opens up interesting gateways into the female condition. Taking this assumption as a starting point, I examine how migration, impacting on the mother–daughter relationship, can redefine gender roles and challenge models of femininity, which are culturally, socially, geographically, and linguistically embedded. I investigate this aspect from a linguistic perspective, through a reading of a corpus of narratives written by four Italian-Canadian writers. The movement from Italy to Canada enacts ‘the emergence of alternative family romances’ and draws new routes to femininity. This paper seeks to illustrate how, in the narratives I examine, these new routes are explored through linguistic means. The authors in my corpus use code-switching to highlight contrasting views of femininity and reposition themselves with respect to politics of gender.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.019 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".