Italian Canadian Writers in Ontario: The Function of Place
Bibliographic record
Abstract
The Italian Canadian writers who live and work in Ontario can hardly be regarded as a homogeneous group.In first approaching the subject I could think of only two things these diverse poets and novelists had in common: they share an immediate Italian immigrant background, and they are distinctly urban in their artistic orientation.I decided, therefore, to search for some facet present in the works of Mazza, Di Cicco, and Di Michele, and novelists Paci and Ardizzi which could be considered the particular perspective these writers bring into contemporary Canadian letters.I undertook to examine the places each writer describes to determine if a view of their surroundings, in a land where their families are newcomers, might contribute a literary element which poets and novelists born into «English» Ontario do not convey.Exploring this provided me with a fascinating journey from the source to the cityscape to the psychic interiors these writers describe so well.For the sheer splendour of his evocative language, this place imagery of his Calabrian childhood by Antonio Mazza captures the reader's attention immediately.!OUR HOUSE IS IN A COSMIC EAR In a cosmic ear of sharp peaks and stepped hills where broom and cyclamen bloom side by side with the lemon trees is the house where I was born.This house... let's look at it from a childish point of view.A village of bells crowded in the velvet street, no sidewalks.Sunday morning, no Monday.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.042 | 0.012 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".