Bibliographic record
Abstract
In this chapter I examine the ordering of the lexical items discussed in chapter 4.1 argue that evidence exists, both within individual languages and across the family, for the following generalizations: (i) Elements in a fixed scopal relationship occur in a fixed order with respect to each other. (ii) Elements in which the scopal relationship can be reversed occur in variable order, with interpretation related to order. (iii) Elements that do not enter into a scopal relationship with each other may occur in different orders, both within a particular language and across the family. As a result, elements that are related by scope have a predictable ordering, whereas ones that do not enter into a scopal relationship must have their ordering stipulated for the particular language. On the Ordering of the Lexical Items In the Athapaskan literature, statements are often made to the effect that ordering is relatively fixed within the functional (conjunct) complex across the family, but that when the lexical (disjunct) complex is examined, ordering varies. For instance, Cook 1989:194–195, in a discussion of the numbering of verb prefix positions, states that “pan-Athapaskan prefix order may be established at least for the conjunct prefixes where the differences in prefix categories are primarily in the disjunct prefixes.” Kari 1989:449 makes this point explicitly: “the innermost prefixes [functional – KR] are more directly comparable across the languages than are the outermost prefixes [lexical – KR].”
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".