Bibliographic record
Abstract
Athapaskan languages have verbs that are extraordinarily complex, and that pose a challenge to theories of morphosyntactic structure (see, for example, Aronoff 1994, Hargus 1988, Rice 1993, 1998, Speas 1984, 1987, 1990, 1991a,b, Spencer 1991, Travis 1992, for discussion). The verb word is complex in many ways: it is morphologically rich, the surface ordering of morphemes is apparently without reason, discontinuous dependencies are frequent, and blocking effects between morphemes of identical shape but different meaning are abundant. The goal of this chapter is to outline the structure of an Athapaskan verb as traditionally described and to examine the claim that a template is required to define the ordering of morphemes within the verb. The Templatic Nature of the Athapaskan Verb As discussed in chapter 1, the verb in Athapaskan languages is typically described as consisting of a template, or string of fixed order positional classes. The template orders the morphemes, and each morpheme is marked lexically for the position in the template that it occurs in. In addition, phonological boundary types are lexically associated with the different morphemes in order to account for their phonological properties. A template for Slave ([slevi]), adapted from Rice 1989, is given in (1). See appendix 1 for a list of templates proposed in the literature for a number of languages of the family. Terminology will be clarified throughout the book; I do not attempt to define terms here.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.039 | 0.008 |
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".