Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it