roles of efficiency and complexity in the processing of verb particle constructions
Bibliographic record
Abstract
Recent theories have proposed that processing difficulty affects both individuals’ choice of grammatical structures and the distribution of these structures across languages of the world (Hawkins, 2004). Researchers have proposed that performance constraints, such as efficiency, integration, and storage costs, drive languages to choose word orders that minimize processing demands for individual speakers (Hawkins, 1994; Gibson, 2000). This study investigates how three performance factors, adjacency, dependency, and complexity, affect reading times for sentences with verb-particle constructions. Results from a self-paced reading task indicate that reading times increase with each performance factor, such that shifted sentences, more dependent verb-particle constructions, and more complex noun phrases are more difficult. More importantly, I explore the relative weightings and interactions of the performance factors. The results support the notion that processing ease affects grammaticalization, such that those structures which are more easily processed by individuals (subject relatives and adjacent dependent constituents) are also more common crosslinguistically (Keenan & Hawkins, 1987).
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".