Bibliographic record
Abstract
04–533Cheng, Winnie and Warren, Martin (Hong Kong Polytechnic U., Email: egwcheng@polyu.edu.hk ). Peer assessment of language proficiency. Language Testing (London, UK), 22, 1 (2005), 93–121. 04–534Malabonga, Valerie, Kenyon, Dorry M. and Carpenter, Helen (Centre for Applied Linguistics, Washington, USA; Email: valerie@cal.org ). Self-assessment, preparation and response time on a computerised oral proficiency test. Language Testing (London, UK), 22, 1 (2005), 59–92. 04–535Parkinson, Jean and Adendorff, Ralph (U. of Natal, India). The use of popular science articles in teaching scientific literacy. English for Specific Purposes (Oxford, UK), 23, 4 (2004), 379–396. 04–536Quinn, M. (Melbourne U., Australia). Talking with Jess: Looking at how metalanguage assisted explanation writing in the Middle Years. Australian Journal of Language and Literacy (Norwood, South Australia), 27, 3 (2004), 246–261. 04–537Raphael, T. E., Florio-Raine, S. and George, M. (Oakland U., Australia). Book club plus: organising your literacy curriculum to bring students to high levels of literacy. Australian Journal of Language and Literacy (Norwood, South Australia), 27, 3 (2004), 198–216. 04–538Reed, Malcolm (U. of Bristol, UK). Write or wrong? A sociocultural approach to schooled writing. English in Education (Sheffield, UK), 38, 1 (2004), 21–38. 04–539Ren, Guanxin. Introducing oval writing.Babel – Journal of the AFMLTA (Queensland, Australia), 39, 1 (2004), 4–10. 04–540Richgels, Donald J. (Northern Illinois U., USA; Email: richgels@niu.edu ). Paying attention to language. Reading Research Quarterly (Newark, USA), 39, 4 (2004), 470–477. 04–541Sang-Keun, Shin (Ewha Womens U. Seoul, Korea; Email: sangshin@ewhaac.kr ). Did they take the same test? Examinee language proficiency and the structure of language tests. Language Testing (London,UK), 22, 1 (2005), 31–57. 04–542Schoonen, Rob (U. of Amsterdam, The Netherlands; Email: rob.schoonen@uva.nl ). Generalisability of writing scores: an application of structural equation modelling. Language Testing (London, UK), 22, 1 (2005), 1–30. 04–543So, Bronia (U. of Hong Kong, Hong Kong; Email: bronia_so@yahoo.com.hk ). From analysis to pedagogic applications: using newspaper genres to write school genres. Journal of English for Academic Purposes (Oxford, UK), 4, 1 (2005), 67–82. 04–544Spodark, Edwina (Hollins U., USA; Email: spodark@hollins.edu ). “French in Cyberspace”: an online French course for undergraduates. CALICO Journal (Texas, USA), 22, 1 (2004), 83–101. 04–545Sutherland-Smith, Wendy (Deakin U., Australia; Email: wendyss@deakin.edu.au ). Pandora's box: academic perceptions of student plagiarism in writing. Journal of English for Academic Purposes (Oxford, UK), 4, 1 (2005), 83–95. 04–546Thurstun, Jennifer (Macquarie U., Australia). Teaching and learning the reading of homepages. Prospect (Sydney, Australia), 19, 2 (2004), 56–71. 04–547Valencia, S. W. and Riddle Buly, M. (Washington U., USA). Behind test scores: What struggling readers REALLY need. Australian Journal of Language and Literacy (Norwood, South Australia), 27, 3 (2004), 217–233. 04–548Warschauer, Mark (U. of California, USA; Email: markw@uci.edu ), Grant, David, Del Real, Gabriel and Rousseau, Michele. Promoting academic literacy with technology: successful laptop programs in K-12 schools. System (Oxford, UK), 32, 4 (2004), 525–537. 04–549Young, Richard F. and Miller, Elisabeth R. (U. of Wisconsin, USA; Email: rfyoungt@wisc.edu ). Learning as changing participation: discourse roles in ESL writing conferences. The Modern Language Journal (Malden, MA, USA), 88, 4 (2004), 519–535. 04–550Bernhardt, Elizabeth B., Rivera, Raymond J. and Kamil, Michael L. (Stanford U., USA). The practicality and efficiency of web-based placement testing for college-level language programs. Foreign Language Annals (Alexandria, VA, USA), 37, 3 (2004), 356–366. 04–551Brown, Gavin T. L. (U. of Auckland, New Zealand; Email: gt.brown@auckland.ac.uz ), Glasswell, Kath and Harland, Don. Accuracy in the scoring of writing: Studies of reliability and validity using a New Zealand writing assessment system. Assessing Writing (New York, USA), 9, 2 (2004), 105–121. 04–552Hawkey, Roger and Barker, Fiona (Cambridge ESOL, UK; Email: roger@hawkey58.freeserve.co.uk ). Developing a common scale for the assessment of writing. Assessing Writing (New York, USA), 9 (2004), 122–159. 04–553Peterson, Shelley, Childs, Ruth and Kennedy, Kerrie (U. of Toronto, Canada; Email: slpeterson@oise.utoronto.ca ). Written feedback and scoring of sixth-grade girls' and boys' narrative and persuasive writing. Assessing Writing (New York, USA), 9 (2004), 160–180. 04–554Watson Todd, Richard (King Mongkut's U. of Technology Thonburi, Thailand; Email: irictodd@kmutt.ac.th ), Glasswell, Kath and Harland, Don. Measuring the coherence of writing using topic-based analysis. Assessing Writing (New York, USA), 9, 2 (2004), 85–104.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.427 | 0.294 |
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".