Bibliographic record
Abstract
This paper gives a novel analysis of the logical structure underlying three classes of vague adjectival predicates (relative adjectives, i.e., tall; total adjectives, i.e., straight; and partial adjectives, i.e., wet) and the realisation of this structure in arguments formed with comparative constructions (i.e., John is taller than Mary). I analyse three classes of valid arguments that can be formed with different types of gradable predicates in comparative constructions: scalarity arguments (i.e., Mary is taller than John and John is tall Mary is tall), maximality arguments (i.e., Stick A is straighter than Stick B Stick B is not straight), and evaluativity arguments (i.e., This towel is wetter than that towel this towel is wet) I show how a previously proposed multi-valued logical system (called Delineation TCS; see Burnett, 2012b; Burnett, 2013) based on the trivalent framework of (Cobreros et al., 2012) can be used to model the reasoning patterns described in the paper. In this system, we derive the scalarity, maximality and evaluativity properties of different classes of adjectives from statements about the properties of cognitive indifference relations associated with these predicates. I therefore conclude that systems such as DelTCS give us valuable insight into the relationship between cognitive judgements of indifference and reasoning patterns with scalar terms.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".