Lexical fixedness and compositionality in L1 speakers’ and L2 learners’ intuitions about word combinations: Evidence from Italian
Bibliographic record
Abstract
The present investigation focuses on first language (L1) and second language (L2) speakers’ sensitivity to lexical fixedness and compositionality of Italian word combinations. Two studies explored language users’ intuitions about three types of word combinations: free combinations, collocations, and idioms. In Study 1, Italian Verb+Noun combinations were embedded in sentential contexts, with control conditions created by substituting the verb with a synonym. L1 and L2 speakers rated sentence acceptability. In Study 2, the original verb was removed from sentences. Participants chose the verb from the list provided they felt was most acceptable. Computational measures were used to measure compositionality of word combinations. Mixed-effects modelling revealed that L1 and L2 speakers judged target word combinations differently in terms of lexical fixedness. In line with phraseological models, L1 speakers judged the use of a synonym as less acceptable in collocations than free combinations. On the contrary, L2 learners judged the use of a synonym as more acceptable in collocations than free combinations. However, all participants perceived idioms as least flexible of the three combination types. Results further showed an interesting effect of compositionality on the speakers’ intuitions about the use of word combinations. Taken together, the findings provide new insights into how L1 and L2 speakers perceive word combinations that vary along the continua of lexical fixedness and compositionality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".