Bibliographic record
Abstract
In order to properly classify the phraseme (that is, a constrained, or non-free, expression) No parking, a universal typology of lexical phrasemes is proposed. It is based on the following two parameters:• The nature of constraints— Lexemic phrasemes: the expression is constrained with respect to freely constructed meaning.— Semantic-lexemic phrasemes: the expression is constrained/non-constrained with respect to the meaning constrained by the conceptual representation.— Pragmatemes: the expression is constrained with respect to pragmatic conditions, that is, to the extralinguistic situation of its use (in a letter, on a street sign, on a package of perishable food).• The compositionalityThe expression can/cannot be represented as regular “sum” of its components.As a result, we have, firstly, the following major classes of lexical phrasemes:1) Non-compositional lexemic phrasemes: idioms (˹cold feet˺, ˹shoot the breeze˺)2) Compositional lexemic phrasemes: collocations (rain heavily, pay a visit)3) Non-compositional semantic-lexemic phrasemes: nominemes (Big Dipper, New South Wales)4) Compositional semantic-lexemic phrasemes: clichés (See you tomorrow! | Absence makes the heart grow fonder.)For clichés, the least-studied class of phrasemes, a more detailed classification is proposed (as a function of the type of their denotation). Secondly, each phraseme (except a nomineme) and each lexemes can be pragmatically constrained, i.e. a pragmateme: ˹Fall out!˺ (idiom; a military command) | Take aim! (collocation; a military command) | Emphasis mine/added (cliché; in a printed text) | Rest! (lexeme; a military command).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".