Transitional probability predicts native and non‐native use of formulaic sequences
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
Formulaic sequences ( FSs ), or prefabricated multi‐word structures (e.g. on the other hand), are often difficult to identify objectively, and current corpus‐driven methods yield structurally incomplete, overlapping, or overly extended structures of questionable psychological validity and pedagogical usefulness. To address these limitations, this study evaluated transitional probability as a potential metric to improve the identification of FSs by presenting 100 four‐word sequences from the B ritish N ational C orpus, varying in transitional probabilities between words, to native and non‐native speakers of E nglish ( N = 293) in a sequence completion task (e.g. for the sake__). Results revealed that the application of transitional probability reduces many of the problems associated with current approaches to FS identification and can produce lists of FSs that are more functionally salient and psychologically valid.
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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 | 0.001 |
| 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