Bibliographic record
Abstract
While it is clear that some languages have a grammatical mass/count distinction (e.g. English), in other languages (e.g. Inuktitut) it is not so obvious. In this paper, I show that Inuttut (Labrador Inuktitut) has a subtle grammatical mass/count distinction: while number, numerals, and most quantifiers do not disambiguate between mass and count nouns, in a few places, the morphology or the semantics disambiguates between mass and count. Thus, Inuttut is not a counterexample to Doetjes (1997) or Chierchia (2010), who both argue that all languages distinguish between mass and count. I further argue against Borer (2005) who claims all nouns in all languages are underlyingly neutral, and are assigned mass interpretation by default in the absence of individuation. I show that Inuttut nouns cannot all be underlyingly neutral and/or mass by default. Keywords: mass; count; classifiers; Inuktitut; number; numerals; quantifiers
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| 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".