The cognitive mechanisms involved in the “DEGREE ADVERB + PROPER NAME” construction
Bibliographic record
Abstract
Abstract There are broad disagreements between existing models regarding the mental representations and processes involved in the “ DEGREE ADVERB + PROPER NAME ” construction, including divergences regarding the semantics of the degree device, the category status of the proper name, the construction’s expressed meaning, its compositionality, and, crucially, the operation holding between the degree device and the proper name. Our corpus-based investigation of two competing models from Construction Grammar and Formal Semantics shows that while both make useful contributions to the scientific understanding of the construction, neither is empirically adequate. Most importantly, we find that the construction participates in several non-predicted expressed meanings; multivariate analyses show that the three meanings amenable to statistical analysis cluster with different semantic usage-features. We argue that the best way to account for the construction’s semantics/pragmatics is via a previously-dismissed cognitive mechanism: an enrichment/strengthening-type operation whereby a pragmatically-supplied scale is added to the message.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".