MétaCan
Menu
Back to cohort
Record W2773087945 · doi:10.1111/1467-968x.12120

Monotonicity In Word Formation: The Case Of Italo‐Romance Result State Adjectives

2018· article· en· W2773087945 on OpenAlexaff
Delia Bentley

Bibliographic record

VenueTransactions of the Philological Society · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsBentley (Canada)
Fundersnot available
KeywordsMonotonic functionWord (group theory)LinguisticsInterpretation (philosophy)State (computer science)Meaning (existential)RomanceAdjectiveScope (computer science)Romance languagesComputer scienceMathematicsNatural language processingPsychologyPhilosophyNounEpistemologyAlgorithm

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.262
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Philological SocietySame topicSyntax, Semantics, Linguistic VariationFrench-language works237,207