Brazilian Portuguese, Syntax and Semantics
Bibliographic record
Abstract
This book opens with Angelika Kratzer and Luigi Rizzi talking about contemporary issues, such as non-recursiveness of focus and the semantics of topics. The chapters climb down the spine from the left periphery to DP: the value of subjunctive across the history of German, expressive expressions in Brazilian Portuguese, left and right dislocation and the speaker’s perspective in Italian, Brazilian double subjects and left dislocated topic, long versus short wh-movement in Brazilian Portuguese and Quebec French, low adverbs and the raising of the verb in Brazilian Portuguese, ellipsis and null objects in Brazilian and European Portuguese, and bare singulars in Brazilian Portuguese. The chapters propose original accounts for language variation and historical changes, most of them focusing on Brazilian Portuguese, a challenge to syntax and semantics. Thus, the volume contributes to Brazilian and Portuguese Linguistics, as well as to general and contemporary research on syntax and semantics of natural languages.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".