Bibliographic record
Abstract
05–587Allwright, Dick (U of Lancaster; r.allwright@lancaster.ac.uk ), Developing principles for practitioner research: the case of exploratory practice. The Modern Language Journal (Malden, MA, USA) 89.3 (2005), 353–366. 05–588De Klerk, Vivian (Rhodes U, South Africa; v.deklerk@ru.ac ), The use ofactuallyin spoken Xhosa English: a corpus study. World Englishes (Oxford, UK) 24.3 (2005), 275–288. 05–589E, He An (The Hong Kong Institute of Education, Hong Kong, China), Use of verbs in teacher talk: a comparison study between LETs and NETs. Hong Kong Journal of Applied Linguistics (Hong Kong, China) 9.2 (2004), 38–54. 05–590Erdener, V. Doǧu & Denis K. Burnham (U of Western Sydney, Australia; d.erdener@uws.edu.au ), The role of audiovisual speech and orthographic information in nonnative speech production. Language Learning (Malden, MA, USA) 55.2 (2005), 191–228. 05–591Hosoda, Yuri (Kanagawa U, Japan), Directives and assessments in Japanese native and nonnative conversation. JALT Journal (Tokyo, Japan) 27.1 (2005), 5–31. 05–592Hu, Xiaoling, Nigel Williamson & Jamie McLaughlin (U of Sheffield, UK; x.l.hu@sheffield.ac.uk ), Sheffield corpus of Chinese for diachronic linguistic study. Literary and Linguistic Computing (Oxford, UK) 20.3 (2005), 281–293. 05–593Hudson, Richard (U College London, UK) & John Walmsley, The English Patient: English grammar and teaching in the twentieth century. Journal of Linguistics (Cambridge, UK) 41.3 (2005), 593–622. 05–594Johnson, Greer Cavallaro (Griffith U, Australia; g.johnson@griffith.edu.au ), Simon Clarke & Neil Dempster, The discursive (re)construction of parents in school texts. Language and Education (Clevedon, UK) 19.5 (2005), 380–399. 05–595Ohta, Amy Snyder (U of Washington, USA; aohta@u.washington.edu ), Interlanguage pragmatics in the zone of proximal development. System (Amsterdam, the Netherlands) 33.3 (2005), 503–517. 05–596Pica, Teresa (U of Pennsylvania, Philadelphia, USA; teresap@gse.upenn.edu ), Classroom learning, teaching, and research: a task-based perspective. The Modern Language Journal (Malden, MA, USA) 89.3 (2005), 339–352. 05–597Sardinha, Berber (Pontifícia Universidade Católica de São Paulo (PUC-SP), Brazil), A influência do tamanho do corpus de referência na obtenção de palavaras-chave usando o programa computacional WordSmith Tools [The influence of reference corpus size on WordSmith Tools keywords extraction]. The ESPecialist (São Paulo, Brazil) 26.2 (2005), 183–204. 05–598Seedhouse, Paul (U of Newcastle upon Tyne, UK; paul.seedhouse@ncl.ac.uk ), ‘Task’ as research construct. Language Learning (Malden, MA, USA) 55.3 (2005), 533–570. 05–599Spada, Nina (U of Toronto, Canada; nspada@oise.utoronto.ca ), Conditions and challenges in developing school-based SLA research programs. The Modern Language Journal (Malden, MA, USA) 89.3 (2005), 328–338. 05–600Von Staa, Betina (Pontifícia Universidade Católica de São Paulo (PUC-SP), Brazil), Deselvomiento de interpretações literárias lógicas e coerentes [Development of loigical and coherent literary interpretations]. The ESPecialist (São Paulo, Brazil) 26.2 (2005), 157–181. 05–601Wong, Jock (Australian National U, Canberra, Australia; jock.wong@anu.edu.au ), ‘Why you so Singlishone?’A semantic and cultural interpretation of the Singapore English particleone. Language in Society (Cambridge, UK), 34.2 (2005), 239–275.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.338 | 0.209 |
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".