Bibliographic record
Abstract
Abstract This entry explores the formal and distributional properties of evaluative morphology. The defining property of evaluative morphology is its interpretive function: it serves to express the speaker's evaluation of the referent (or situation) though the dimension of evaluation is not uniform: it may be related to size or attitude. The distributional properties of evaluative morphology are reviewed, and it is concluded that it does not comprise a unified morphological class. This observation has long occupied morphologists and has led to various proposals regarding the morphological classification of evaluative morphology. Most notably Scalise has proposed a ‘third morphology’ which – in terms of application of morphological rules – is situated between derivation and inflection. This approach faces the problems intrinsic to any approach which seeks to develop a unified treatment for evaluative morphology, however. It is shown that a morphosyntactic approach towards evaluative morphology can come to terms with the observed variation: evaluative morphology is not special in that it can be realized in any way in which linguistic elements can integrate to form complex expressions. Specifically, there are two parameters of variation: (i) whether a given evaluative morpheme is realized as a head or as a modifier and (ii) where in the functional hierarchy it is realized. As such, its syntactic properties in part determine its interpretive properties.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".