The Language of Power: Casini and Bancheri's In-Depth Analysis of Italian in Immigration and Global Contexts
Bibliographic record
Abstract
Abstract This review article looks at a groundbreaking book in sociolinguistics by Simone Casini and Salvatore Bancheri, What Is the Language of Power? Theoretical Reflections on Italian, Italiese and Other Languages, which focuses on the forms and uses of Italian in immigrant communities, as well as the role of the Italian language in the global village in which we now reside. Based on research conducted on the kind of Italian that takes shape in immigrant communities, called “Italiese,” one of the main insights that can be gleaned from this penetrating book is that the language that emerges in immigrant communities is a product of creative mechanisms, enlisted unconsciously to render the native language (or dialect) adapted to solving everyday communicative problems that pertain to the new environment socially and conceptually. By making the English input conform to the native language, structurally and semantically, the result is a code that allows for direct access to the new reality on its own terms. The book also relates the ways in which Italiese is constructed and employed to the history of Italian itself, ending with a broad examination of the roles that the language should be playing in an international context today. As such, it provides an expansive theoretical framework for assessing the factors that contribute to making a language, such as Italian, an instrument of control over any environment, real or virtual—hence, a “language of power.”
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".