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Record W4391902288 · doi:10.5406/23256672.100.2.07

Stiamo (ancora) tutti bene? L'italiano all'estero: dai primi numeri MLA post-pandemia al mercato del lavoro. Il ‘caso’ della GTA di Toronto

2023· article· it· W4391902288 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueItalica · 2023
Typearticle
Languageit
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.003

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.

Opus teacher head0.027
GPT teacher head0.274
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it