Bibliographic record
Abstract
06–01Akira, Mochida & Harrington, Michael (U Queensland, Australia), The Yes/No test as a measure of receptive vocabulary knowledge. Language Testing (Hodder Arnold) 23.1 (2006), 73–98. 06–02Biddle, Rodney (Gunma Prefectural Women's U, Japan), What makes a good English class? Perceptions of individuality and the group among Japanese EFL students. The Language Teacher (Japan Association for Language Teaching) 29.8 (2005), 3–8. 06–03Burden, Peter (Okayama Shoka U, Japan), The castor oil effect: Learner beliefs about the enjoyment and usefulness of classroom activities and the effects on student motivation. The Language Teacher (Japan Association for Language Teaching) 29.10 (2005), 3–9. 06–04Corbeil, Giselle (Acadia U, Canada), Effectiveness of focus on forms instruction: Different outcomes on constrained and free production tasks?Canadian Journal of Applied Linguistics (Canadian Association of Applied Linguistics) 8.1 (2005), 27–46. 06–05Dastjerdi, Hossein Vahid, Talebinezhad & Mohammad Reza (U Isfahan, Iran), Chain-preserving deletion procedure in cloze: A discoursal perspective. Language Testing (Hodder Arnold) 23.1 (2006), 58–72. 06–06Guan Eng Ho, Debbie (U Brunei Darussalam, Brunei; debbieho@fass.ubd.edu.bn ), Why do teachers ask the questions they ask?RELC Journal (Sage) 36.3 (2005), 297–310. 06–07Honna, Nobuyuki (Aoyama Gakuin U, Japan; honna@sipeb.aoyama.ac.jp ) & Yuko Takeshita, English language teaching in Japan: Policy plans and their implementations. RELC Journal (Sage) 36.3 (2005), 363–383. 06–08Jenkins, Jennifer (King's College, U London, UK), Implementing an international approach to English pronunciation: The role of teacher attitudes and identity. TESOL Quarterly (Teachers of English to Speakers of Other Languages) 39.3 (2005), 535–543. 06–09Kato, Asako (Fudoka Seiwa High School, Japan), The visual text speaks louder than the written text: An examination of the revised Monkasho English I textbooks. The Language Teacher (Japan Association for Language Teaching) 29.5 (2005), 3–13. 06–10Lazaraton, Anne (U Minnesota, Minneapolis, USA; lazaratn@umn.edu ) & Noriko Ishihara, Understanding second language teacher practice using microanalysis and self-reflection: A collaborative case study. The Modern Language Journal (Blackwell) 89.4 (2005), 529–542. 06–11Li, Defeng (Chinese U Hong Kong, China; defeng@cuhk.edu.hk ), Teaching of specialized translation courses in Hong Kong: A curricular analysis. Babel (John Benjamins) 51.1 (2005), 62–77. 06–12McCaughey, Kevin (California, USA; kevin@kevinmccaughey.com ), Thekashasyndrome: English language teaching in Russia. World Englishes (Blackwell) 24.4 (2005), 455–459. 06–13McEachron, Gail (College of William and Mary, VA, USA) & Ghazala Bhatti, Language support for immigrant children: A study of state schools in the UK and US. Language, Culture and Curriculum (Multilingual Matters) 18.2 (2005), 164–180. 06–14Reza Hashemi, Mohammad & Farah Gowdasiaei (Ferdowsi U Mashhad, Iran; smrh@ferdowsi.um.ac.ir ), An attribute-treatment interaction study: Lexical-set versus semantically unrelated vocabulary instruction. RELC Journal (Sage) 36.3 (2005), 341–361. 06–15Savickienė, Ineta & Violeta Kalėdaitė (Vytautas Magnus U, Kaunas, Lithuania), Cultural and linguistic diversity of the Baltic states in a new Europe. Journal of Multilingual and Multicultural Development (Multilingual Matters) 26.5 (2005), 442–452. 06–16Sercu, Lies (Katholieke Universiteit Leuven, Belgium), María del Carmen Méndez García & Paloma Castro Prieto, Culture learning from a constructivist perspective: An investigation of Spanish foreign language teachers' views. Language and Education (Multilingual Matters) 19.6 (2005), 483–495. 06–17Stempleski, Susan (City U New York, USA), Developing fluency: Some suggestions for the classroom. The Language Teacher (Japan Association for Language Teaching) 29.6 (2005), 31–33. 06–18Swan, Michael (Freelance), Legislation by hypothesis: The case of task-based instruction. Applied Linguistics (Oxford University Press) 26.3 (2005), 376–401. 06–19Ter-Minasova, Svetlana G. (Moscow State U, Russia; dean@ffl.msu.ru ), Traditions and innovations: English language teaching in Russia. World Englishes (Blackwell) 24.4 (2005), 445–454.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.516 | 0.366 |
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".