Bibliographic record
Abstract
Italian emigrants in Canada (with strong concentrations in Toronto and Montreal) were employed mainly in the field of building and road construction. This led to the establishment of an Italian community of around one and a half million people between the two major cities, which, unlike what occurred in Great Britain and the U.S.A., rendered not only economic production but a cultural one as well. In no other nation other than Canada did Italian emigrants produce such an important and homogenous literary body, so much so as to be considered (and accepted) as an unequivocal component of literature in what has become their second country. Au Canada, les émigrés italiens (fortement concentrés dans les villes de Toronto et Montréal) ont essentiellement été employés dans le bâtiment et la construction des routes. Entre ces deux métropoles, une communauté italienne d’environ un million et demi de personnes s’est constituée et a généré, contrairement à ce qui s’est produit au Royaume Uni et aux USA, des retombées non seulement économiques mais aussi culturelles. En effet, nulle part ailleurs qu’au Canada, l’émigration italienne n’a su produire un aussi important et homogène corpus littéraire considéré (et accepté) comme une composante incontournable de l’histoire littéraire de ce pays devenu la seconde patrie.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.023 | 0.016 |
| Scholarly communication | 0.011 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".