The American Association of Teachers of Italian and the Canadian Connection
Bibliographic record
Abstract
In his history on the teaching of Italian in the United States, Fucilla has discussed the role played by Italian patriots who left Italy for political reasons to seek refuge in America.The year 1779 marks the first significant date in the establishment of Italian studies in the United States, at the College of William and Mary in Virginia by Carlo Bellini.'An Italian refugee from the grandduchy of Tuscany, some 60 years later, in 1841, Louis Mariotti, alias Antonio Gallenga, a political refugee from Parma, introduced Italian into Canada, at King's College in Windsor, Nova Scotia."Gallenga, described as a respectable and highly accomplished scholar, recommended by George Ticknor, first Smith Professor at Harvard in 1816, had taught at the Harvard Young Ladies Academy from 1836 to 1839 and published two volumes on Italian history and literature and in the North American Review.Gallenga who could not endure the harsh winter and restricted social life of eastern Canada soon left for England and University College in London.At the secondary school level Italian is listed as a subject in the calendar of Upper Canada Academy in Cobourg, Ontario in 1840.The textbooks listed in the program were those used in the United States, such as Graglia's Grammar, adopted in 1827 at Harvard, and Vergani's Grammar, praised as one of the best imported from Italy.Even at this date there was a Canadian connection with the United States.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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".