Terza missione universitaria nelle discipline umanistiche: il ruolo dei dipartimenti di italianistica all'estero
Bibliographic record
Abstract
The paper deals with an innovative field of investigation on Italian language abroad which addresses the relationship between language, culture, society and economy by focus- ing on strategies of the so-called ‘third mission’ of universities and departments of Italian studies abroad. Our aim is to explore which strategies, specifically of community engage- ment of the universities with their local communities, have emerged in the context of Italian departments abroad towards communities of Italian origin and students of Italian abroad. \nThe work develops a framework to analyze the community engagement of Italian departments abroad starting from three factors: i) the institutional strategies of the university towards the third mission, ii) the Italian language teaching strategies of Italian departments, and iii) the role of communities of Italian origin in the place where the university is locat- ed. The framework is adopted in order to analyze three cases of universities, Manchester, Sydney and Toronto, located in cities that both have been the object of strong immigration by Italians throughout history and are home to renowned departments of Italian Studies. \nThe framework has been developed around the case of Italian language abroad, but can lend itself to other linguistic contexts as well.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".