In support of multiword unit classifications: Corpus and human rating data validate phraseological classifications of three different multiword unit types
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
Abstract Multiword units (MWUs) are word combinations which sit within the continuum of formulaic language. Many experimental studies have focused on the online processing of MWUs by native and non-native speakers, and the processing of idioms in particular. However, some studies use a mix of various MWU subtypes, while other studies have varying definitions for the same subtypes. For results from MWU studies to be useful to theories of language processing, storage and access, clearer classifications are needed for MWU subtypes. This study aims to empirically validate MWU categories as described by certain phraseologists in the European tradition. This will be done using MWUs from the British National Corpus, from across the continuum of frequent to infrequent occurrence and co-occurrence. Hence, in this paper I will describe the empirical findings that may validate the classifications for MWU categories of restricted collocations, idioms, and lexical bundles, using corpus-based measures and human ratings.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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