Formulaic Language Acquisition and Production: Implications for Teaching
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 language units, ready-made chunks and sequences of words, have been the subject of a large and growing body of research. Although formulaic language has been largely overlooked in favor of models of language that center around the rule-governed, systematic nature of language and its use, there is increasing evidence that these multiword lexical units are integral to first- and second-language acquisition, as they are segmented from input and stored as wholes in long-term memory. They are fundamental to fluent language production, as they allow language production to occur while bypassing controlled processing and the constraints of short-term memory capacity. This article defines and describes formulaic language units and surveys the research evidence of their role in language acquisition and production. The implications of this knowledge for classroom teaching are considered, with particular emphasis on attending to input and fostering interaction to facilitate the acquisition of a repertoire of formulaic language.
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) | 0.115 | 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