In support of multiword unit classifications: Corpus and human rating data validate phraseological classifications of three different multiword unit types
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.
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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 | 0.013 | 0.094 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".