Figuring Out How Verb-Particle Constructions Are Understood During L1 and L2 Reading
Bibliographic record
Abstract
The aim of this paper was to investigate first-language (L1) and second-language (L2) reading of verb particle constructions (VPCs) among English-French bilingual adults. VPCs, or phrasal verbs, are highly common collocations of a verb paired with a particle, such as eat up or chew out, that often convey a figurative meaning. VPCs vary in form (eat up the candy vs. eat the candy up) and in other factors, such as the semantic contribution of the constituent words to the overall meaning (semantic transparency) and form frequency. Much like classic forms of idioms, VPCs are difficult for L2 users. Here, we present two experiments that use eye-tracking to discover factors that influence the ease with which VPCs are processed by bilingual readers. In Experiment 1, we compared L1 reading of adjacent vs. split VPCs, and then explored whether the general pattern was driven by item-level factors. L1 readers did not generally find adjacent VPCs (eat up the candy) easier to process than split VPCs (eat the candy up); however, VPCs low in co-occurrence strength (i.e., low semantic transparency) and high in frequency were easiest to process in the adjacent form during first pass reading. In Experiment 2, we compared L2 reading of adjacent vs split VPCs, and then explored whether the general pattern varied with item-level or participant-level factors. L2 readers generally allotted more second pass reading time to split vs. adjacent forms, and there was some evidence that this pattern was greater for L2 English readers who had less English experience. In contrast with L1 reading, there was no influence of item differences on L2 reading behavior. These data suggest that L1 readers often have lexicalized VPC representations that are directly retrieved during comprehension, whereas L2 readers are more likely to compositionally process VPCs given their more general preference for adjacent particles, as demonstrated by longer second pass reading time for all split items.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".