Bibliographic record
Abstract
This paper is the first investigation of nominal countability in Kaingang, a Jê language spoken in Brazil. The main claim of this paper is that all Kaingang nouns are lexically count. This hypothesis is supported by a number of morphosyntactic and semantic properties of nouns in the language. Among them two crucial properties emerge: (i) Kaingang allows numerals and other count quantity expressions to combine directly with individual and substance nouns, and (ii) in quantity judgement tasks (Barner & Snedeker 2005) comparisons with both types of nouns are cardinality-based. I analyze this generalized counting strategy as a direct effect of the lexical semantics of nouns. Building on Krifka’s approach (1989; 2007; 2008), I argue that all Kaingang nouns are born quantized, i.e., they are lexically equipped with a context-sensitive built-in counting function that measures quantities in terms of individual- or portion-units. This paper contributes with additional crosslinguistic evidence to two claims: (i) that the mass/count distinction in the nominal domain isn’t a language universal (Wiltschko 2012), and (ii) that the defining property of count nouns is quantization, rather than atomicity (Krifka 1989; 2007).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".