Bibliographic record
Abstract
Many Athabaskan languages have a construction that I call the activity incorporate construction. Activity incorporates are similar in some ways to circumstantial incorporates, entering into non-core thematic relationships with the verb stem. The languages that allow incorporates divide into two major groups based on the treatment of the activity incorporates: in some languages activity incorporates are dependent nouns, functioning as direct objects, while in others they have a suffix that indicates dependency, showing that the activity incorporate is not the head of the construction. In the former, the relationship between the verbal head and the activity incorporate is indicated through the voice/valence system and in the latter, through the suffix on the activity incorporate. Focusing on the incorporate, this looks like a development from parataxis — the activity incorporate and the verb stem are in a non-morphologically marked, loosely construed relationship with one another — to hypotaxis, with overt marking of the relationship between the actions involved. However, when the construction is viewed as a whole, the development of the overt marking of dependency is accompanied by a change in the overt marking of voice/valence. While formally there is a change in structure in terms of how the dependency is indicated, functionally in both cases the dependency relationship between the two elements concerned is indicated in some way, without any change in clausal complexity but rather the location of the complexity.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".