Bibliographic record
Abstract
Over the past few years, I have managed a booth of literary publications at Montreal's "Settimana Italiana".Few people go to a "festival" to look at books or magazines; they'd rather see the fashion show or the live musical performances.Someone told me bluntly that Italian-Canadians do not read.I would qualify that by saying that perhaps not many read their own writers.In fact, many of the Italian-Canadians who visited the booth were not aware that the community has its own literary corpus while others did not distinguish between Italian writers and Italian-Canadian writers.They had not heard of Caterina Edwards, Carmine Starnino, Darlene Madott or Peter Oliva.They may have seen the madefor-TV film Lives of the Saints, starring Sophia Loren, but they did not know or remember that the movie is based on Nino Ricci's trilogy.According to the 2001 Census, 4.30% of Canada's population (1,272,835) give Italian as their ethnic origin and 1.66% say that Italian is their mother tongue (Statistics Canada).Italian-Canadian history has been documented in works by Robert Harney, Bruno Ramirez and John Zucchi.The dominant image of Italian immigrants to Canada is that of poorly educated labourers who left a poverty-stricken postwar homeland in search of a better future for their children.The first generation is most often recognized for its physical contribution to Canada: they are the builders of homes, skyscrapers, bridges and railways.Through their hard work and determination, many immigrants gave their children opportunities to succeed in areas where they could not: in education, medicine, law, business and the arts.Perhaps because of their single-mindedness to succeed materially, the majority of Italian-Canadians has been less attentive to the creative !This paper was originally presented at the Conference "From Emigrante to Canadese: The Italian-Canadian Experience," Ottawa, 14-16
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.073 | 0.026 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".