Brazilian and English bare nouns: Language variation, experiments, and L2s
Bibliographic record
Abstract
This paper proposes to connect research areas with the aim of understanding bare nominals across languages. It focuses on Brazilian Portuguese (BrP) and English. The BrP nominal system challenges the parametric models in Chierchia (1998a, 2010), and two proposals have been raised to explain it within Chierchia’s model: the Bare Singular (BS) is a plural count noun (Schmitt and Munn 1999, 2002; Müller 2002) or it is mass (Pires de Oliveira and Rothstein 2011). Experimental research (Bevilaqua 2019) does not support either of these theories because BrP speakers oscillate between mass and count when interpreting the BS. In contrast, BSs are ungrammatical in English and speakers massify them; they are never counted. Motivated by the experimental studies, Pires de Oliveira (2020, 2021, to appear) presents a new approach: although BrP and English are number marking languages (Chierchia 2010, 2015), atomicity, a grammatical operation (Rothstein 2010, 2017), is activated at different points in the derivation. In English, the first nominal layer projects atomicity, while in BrP, the determiner carries this information; thus, plural inflection is optional on the noun. This paper suggests that language processing and second language acquisition are areas of investigation that may provide new evidence for a better understanding of the semantics of noun phrases.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".