Monotonicity In Word Formation: The Case Of Italo‐Romance Result State Adjectives
Bibliographic record
Abstract
Abstract The Monotonicity Hypothesis (Koontz‐Garboden ) predicts that no productive word formation operations delete any decompositional operators that are part of word meaning. We test this hypothesis examining Italo‐Romance result state participles. First, we consider rhizotonic~arrhizotonic pairs which provide morphologically transparent evidence for the contrast between non‐passive (non‐agentive) and passive (agentive) result state adjectives (e.g., Sicilian cuòttu/cùattu vs. cuciùtu ‘cooked’). Whereas the non‐passive result states of other languages have received decausative accounts, our findings suggest that such non‐monotonic accounts are based on an incorrect interpretation of the results of a key diagnostic test. Broadening the scope of our investigation, we consider other classes of non‐passive result state adjectives, which are indisputably non‐causative and cannot but be formed monotonically. We put forward a monotonic account of the formation of Italo‐Romance result state adjectives, which captures all the classes under investigation and can be extended to the result state adjectives of other languages. Ultimately, this study provides strong support for monotonicity in word formation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".