Production of multiword referential phrases: Inclusion of over-specifying information and a preference for modifier-noun phrases
Bibliographic record
Abstract
We examined the underlying psycholinguistic and cognitive factors that give rise to the production of multiword expressions. For example, if a story describes a woman buying a dog with blue fur, will people include the color of the dog when referring to the animal and, if so, in what syntactic form? In the experiment, participants read short stories that contained a concept that was presented as either a modifier-noun phrase (e.g., the blue dog) or full phrase (e.g., the dog that was blue). We also varied whether the property being highlighted was normal (e.g., brown) or distinctive (e.g., blue) for the head noun concept (e.g., dog). We found that participants are more likely to include distinctive properties than normal properties when referring to the concept. Although the selection of a syntactic form was partially influenced by the form of the information in the story, there was a strong overall bias toward using a modifier-noun phrase structure.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".