Bibliographic record
Abstract
Abstract Natural languages have three major families of phrasemes: - Lexical phrasemes (phraseologized phrases): kick the bucket, black box, pay a visit, if you know what I mean. - Morphological phrasemes (phraseologized wordforms, see [Beck & Mel’čuk 2011]): for+get, light+house, in+dispens+able. - Constructional, or syntactic, phrasemes (phraseologized constructions, or phrase schemata): “X(N) VINF?!?”, as in John be afraid?!? For more details on phrasemes within the Meaning-Text framework, see (Mel’čuk 2015: 336-340). This paper proposes an overview of an important subclass of lexical phrasemes that has not been as yet paid sufficient attention: cliches. First, a typology of lexical phrasemes is presented, with the definitions that underlie the subsequent discussion; this provides a formal framework for the description of cliches (Section 1). Second, the class of cliches is examined and a typology of cliches is proposed, based on the type of the cliche’s referent (Section 2). Third, a dimension for restrictions on the use of lexemic expressions in particular situations of linguistic communication is introduced-namely, a pragmatic constraint. Applied to cliches, it defines a subclass of a subclass of cliches- pragmatemes (Section 3).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".