Stiamo (ancora) tutti bene? L'italiano all'estero: dai primi numeri MLA post-pandemia al mercato del lavoro. Il ‘caso’ della GTA di Toronto
Bibliographic record
Abstract
Abstract Il contributo propone una analisi linguistica e politico-linguistico-educativa sulla attuale condizione dell'insegnamento dell'italiano all'estero, a partire dai dati sul numero delle iscrizioni ai corsi di lingua presentati dalla MLA (2022) e dal College Board (AP courses). Il dato generale sui numeri delle iscrizioni ai corsi e sugli esami AP è poi considerato a titolo esemplificativo nel contesto economico professionale della Greater Toronto Area (GTA) di Toronto, così da fornire un quadro specifico, ma esaustivo, delle opportunità di spesa delle competenze linguistiche in italiano in un contesto come quello della capitale dell'Ontario che è stato nel passato particolarmente attrattivo per l'Italia e l'italiano a seguito degli ingenti movimenti migratori degli anni Settanta e Ottanta.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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".