Bibliographic record
Abstract
Italian Words: A Practical Introduction to the Italian Lexical System offers a detailed description of the Italian lexicon, explaining the organization of Italian words in a system and the function of this system.What is a word and what is typical of Italian words? How are words’ meaning described and how are words assigned to objects? How are words (seen as lexical items) connected? How was the first dictionary of Italian created? What can we learn about the world and about the speaker through the words used? The book provides answers to these, and other similar questions.Key features: - words in a system: the book provides readers with a theoretical framework integrated with practical strategies and effective exercises to enhance readers’ vocabulary acquisition and to develop analytic skills and linguistic competence; - cross-linguistic comparisons: the book offers a broad perspective on language lexical system (in general) and highlights the specific features of the Italian words, comparing them with English, French, and Norwegian words; - cultural insights: the book demonstrates the interconnectedness of language and culture and describes some features of Italian society through the lens of the words analyzed in the book.This book is ideal for intermediate to advanced students of Italian and can be used alongside a grammar or textbook. It will also be a valuable read for anyone interested in or looking to learn more about the Italian language and culture.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.184 | 0.148 |
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".