Effects of Case and Transitivity on Processing Dependencies: Evidence From Niuean
Bibliographic record
Abstract
We investigate the processing of wh questions in Niuean, a VSO ergative-absolutive Polynesian language. We use visual-world eye tracking to examine how preference for subject or object dependencies is affected (a) by case marking of the subject (ergative vs. absolutive) and object (absolutive vs. oblique), and (b) by the transitivity of the verb (whether the object is obligatory). We find that Niuean exhibits (a) an effect of case, whereby dependencies of arguments with absolutive case (whether subjects or objects) are preferred over dependencies of arguments with ergative or oblique case, and (b) an effect of transitivity, whereby dependencies of obligatory objects (i.e., of transitive verbs) are preferred over dependencies of optional objects (i.e., of intransitive verbs). These results constitute evidence against theories that appeal to a universal subject advantage, or to the linear distance between filler and gap. Instead, the effect of case is consistent with a frequency-based account: Because absolutive case has a wider syntactic distribution than ergative or oblique, absolutive dependencies are easier to process. The effect of transitivity reflects sensitivity of the parser to whether or not an argument is obligatory. We propose that these two strategies could be unified if the parser prefers dependencies with arguments that are more likely to materialize.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".