Bibliographic record
Abstract
In Dënesųłıné, an underdocumented indigenous language of Canada, the nominalization of full, finite clauses is highly productive. As a contribution to language description as well as to the study of nominalization, I show many examples of this construction, and give evidence that they are indeed nominalizations. I also show that these nominalizations are used instead of attributive modification of a noun, i.e. instead of adjectives and relative clauses. This fact turns out to be theoretically highly significant, because, as I argue, it is a strong piece of evidence that nouns in Dënesųłıné are inherently entities (type 〈e〉). Based on Chierchia’s (1998) nominal mapping parameter, I develop a typology where nouns in some languages are mapped to type 〈e〉 and, unlike better-known type 〈e〉 languages such as Mandarin, remain of that type throughout the derivation, without ever shifting to the predicative type, 〈e,t〉. I speculate on reasons for the emergence of this kind of language, and based on Dënesųłıné, develop its major characteristics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".