Bibliographic record
Abstract
This article develops a formal semantic and syntactic analysis of distributive numerals in Tlingit, a highly endangered language of Alaska, British Columbia, and the Yukon. Such numerals enforce a ‘distributive reading’ of the sentence, and thus are one instance of the broader phenomenon of ‘distance distributivity’ (Zimmermann 2002). As in many other languages, a Tlingit sentence containing a distributive numeral can describe two distinct kinds of ‘distributive scenarios’: (i) a scenario where the distribution is over some plural entity (e.g. ‘My sons caught three fish each’), and (ii) one where the distribution is over some plural event (e.g. ‘My sons caught three fish each time’) (Gil 1982, Choe 1987, Zimmermann 2002, Oh 2005). Despite this apparent ambiguity, I put forth a univocal semantics for Tlingit distributive numerals, one whereby they consistently invoke quantification over events. Under this semantics, the ability of distributive numerals to describe both kinds of scenarios in (i) and (ii) is due not to an ambiguity, but instead to the sentences having relatively weak truth conditions. In contrast to prior analyses of distributive numerals and distance distributivity, the proposed semantics does not actually make use of distributive operators, but nevertheless retains a rather conservative picture of the syntax-semantics interface. The analysis can also account for certain locality effects noted for distance distributives in Korean and German (Zimmermann 2002, Oh 2005), as well as an intriguing puzzle regarding distributive numerals and pluractionality in Kaqchikel (Henderson 2011). Finally, I show how the analysis can be extended to the well-known case of English ‘binominal each’.*
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".