Bibliographic record
Abstract
1. Introduction: The contribution of formulaic language to fundamental debates in linguistics, Weinert, R. (University of Sheffield, UK) Part 1: Formulaic Language in Acquisition and Pedagogy 2 Acquisition of academic adjective-noun collocations, Schmitt, N., and Li, J. (University of Nottingham, UK) 3. Idiomatic speech: The effect of task variation and target language in the use of formulaic sequences by L2 users of French and Spanish Forsberg, F. (Stockholm University, Sweden) 4. Effectiveness of Text Memorization in EFL Learning of Chinese Students, Dai, Z., and Ding, Y. (Nanjing University, China) 5. Lexical bundles in an EAP corpus, Wood, D. (Carleton University, USA) 6. Lexical bundles in introductory electrical engineering textbooks and ESP materials, Chen, L. (Carleton University, USA) Part 2: Identification and Psycholinguistic Processing of Formulaic Language 7. Formulaicity in Code-Switching: criteria for identifying formulaic sequences, Kazuhiko, N. 8. Lexical bundles and working memory: an ERP study, Tremblay, A. (University of Alberta, Canada) 9. Phonological aspects of the identification and the psycholinguistic processing of formulaic sequences, Lin, P. (University of Nottingham) 10. On the comparative processing of different PLI structures - an eyetracking study, Columbus, G. (University of Alberta, Canada) Part 3: Communicative Functions of Formulaic Language 11. Linguistic survey of marriage vows in the framework of critical discourse analysis, Arakelyan, S. (Eurasia University, Armenia) 12. A text in speech's clothing: Discovering specific functions of formulaic expressions in Beowulf and blogs, Garley, M., Slade, B. and Terkourafi, M. (University of Illinois at Urbana-Champaign, USA) 13. Semantic behavior of Arabic idioms, Abdou, A. (University of Manchester, UK) 14. Formulaicity and translation: a cross-corpora analysis of English formulaic binomials and their Italian translations, Giammarresi, S. (University of Palermo, Italy).
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".