Bibliographic record
Abstract
03–161Busbee, Everette (Jeonju U., South Korea; Email: busbee@jeonju.ac.kr ). ‘Pushing’ written output in the computer lab: Real-time error correction over students' shoulders. English Teaching (Korea), 57, 3 (2002), 279–302. 03–162Dyer, Brenda (Tokyo Women's Christian U., Japan) and Friederich, Lee. The personal narrative as cultural artifact: Teaching autobiography in Japan. Written Communication (Thousand Oaks, CA, USA), 19, 2 (2002), 265–96. 03–163Gascoigne, Carolyn (U. of Nebraska at Omaha, USA). Reviewing reading: Recommend- ations versus reality. Foreign Language Annals (New York, USA), 35, 3 (2002), 343–48. 03–164Hinkel, Eli (Seattle U., USA; Email: elihinkel@aol.com ). Matters of cohesion in L2 academic texts. Applied Language Learning (Presidio of Monterey, CA, USA), 12, 2 (2001), 111–32. 03–165Hyland, Ken (City U. of Hong Kong). Directives: Argument and engagement in academic writing. Applied Linguistics (Oxford, UK), 23, 2 (2002), 215–39. 03–166Kitajima, Ryu (San Diego State U., CA, USA; Email: rkitajim@mail.sdsu.edu ). Enhancing higher order interpretation skills for Japanese reading. CALICO Journal (San Marcos, TX, USA), 19, 3 (2001), 571–81. 03–167Nakayama, Tomoko (U. of South Australia; Email: tomoko.nakayama@unisa.edu.au ). Learning to write in Japanese. Babel (AFMLTA) (N. Adelaide, Australia), 37, 1 (2002), 27–38. 03–168Paltridge, Brian (The U. of Melbourne, Australia; Email: brian.paltridge@aut.ac.nz ). Thesis and dissertation writing: An examination of published advice and actual practice. English for Specific Purposes (Amsterdam, The Netherlands), 21, 2 (2002), 125–43. 03–169Slikas Barber, Karen (Adult Migrant English Prog., Central TAFE, Perth, Australia). The writing of and teaching strategies for students from the Horn of Africa. Prospect (Macquarie U., Sydney, Australia), 17, 2 (2002), 3–17. 03–170Sze, Celine. A case study of the revision process of a reluctant ESL student writer. TESL Canada Journal / La Revue TESL du Canada (Burnaby, BC, Canada), 19, 2 (2001), 21–36. 03–171Woodall, Billy R. (U. of Puerto Rico; Email: bwoodall@english.uprm.edu ). Language-switching: Using the first language while writing in a second language. Journal of Second Language Writing (Norwood, NJ, USA), 11, 1 (2002), 7–28. 03–172Yamashita, Junko (Nagoya U., Japan). Reading strategies in L1 and L2: Comparison of four groups of readers with different reading ability in L1 and L2. ITL Review of Applied Linguistics (Leuven, Belgium), 135–136 (2002), 1–35. 03–173Yates, Robert (Central Missouri State U., USA; Email: ryates@cmsu1.cmsu.edu ) and Kenkel, James. Responding to sentence-level errors in writing. Journal of Second Language Writing (Norwood, NJ, USA), 11, 1 (2002), 29–47.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.162 | 0.084 |
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".