Bibliographic record
Abstract
We discuss evidence in Halkomelem, a Coast Salish language of British Columbia, which supports the hypothesis put forward by Manning and Sag (1999) that a universal passive argument structure (ARG-ST) is complex and has two a-subjects. We argue that morphological and syntactic control phenomena in Halkomelem are best described by saying that an a-subject is accessible, where an a-subject is the first argument on an argument structure list. ARG-ST > The Halkomelem passive data show that two notions of subject are essential for capturing control phenomena. One set of constructions-motion auxiliaries, desideratives, and reflexive causatives-involve linking to the internal a-subject. One construction-the control construction–links to either the highest a-subject or the internal a-subject. Similar conclusions have been drawn for data from Russian (Perlmutter 1984), Philippine languages (Schachter 1984), and other languages of the world. As Manning and Sag (1998) point out, one does not have to draw the conclusion that passive must be given a multilevel syntactic analysis from such data. Rather, their analysis of passive, which posits a complex argument structure, easily accounts for Halkomelem. Control facts in Halkomelem, with examples drawn from both morphological and syntactic constructions, can be added to the catalog of phenomenon that support this view of the passive.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".