MAKING L2 ITALIAN CLASSES INCLUSIVE IN NORTH AMERICA: ACTIVITIES AND SUGGESTIONS
Bibliographic record
Abstract
In the last three years, we have introduced some activities and reflections on neutral language in Italian in our classes in Canada. Starting with the sociolinguistic theories studied and discussed on the subject, we have been incorporating explanations, examples, and realia in our lessons to involve our students in examining the application of strategies used in real life to render Italian a neutral language. This paper will illustrate the theory on which we based our research, provide examples of classroom activities, and then share students’ reactions. We will then indicate how such items could be added to Italian language courses to broaden learners’ cultural awareness. Rendere inclusive le classi di italiano L2 in Nord America: attività e suggerimenti Negli ultimi tre anni abbiamo introdotto nelle nostre classi in Canada alcune attività e riflessioni sul linguaggio neutrale in italiano. Partendo dalle teorie sociolinguistiche che abbiamo studiato e approfondito. Abbiamo iniziato a includere spiegazioni, esempi e realia nelle nostre lezioni per coinvolgere i nostri studenti nello studio delle strategie usate nella vita reale per rendere l’italiano neutrale. Quest’articolo inizia illustrando la teoria sulla quale abbiamo basato la nostra ricerca, con esempi di attività in classe e, in seguito, condivideremo le reazioni delle persone in classe. Inoltre, mostreremo come queste attività possano essere inserite nei corsi di lingua italiana per allargare la prospettiva culturale all’interno delle nostre classi.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".