Bibliographic record
Abstract
05–314Alderson, J. Charles (Lancaster U, UK) & Ari Huhta, The development of a suite of computer-based diagnostic tests based on the Common European Framework. Language Testing (London, UK) 22.3 (2005), 301–320. 05–315Al-Hamly, Mashael & Christine Coombe (Kuwait U, Kuwait), To change or not to change: investigating the value of MCQ answer changing for Gulf Arab students. Language Testing (London, UK) 22.4 (2005), 509–531. 05–316Broadfoot, Patricia M. (U of Bristol, UK), Dark alleys and blind bends: testing the language of learning. Language Testing (London, UK) 22.2 (2005), 123–141. 05–317Cumming, Alister (U of Toronto, Canada; acumming@oise.utoronto.ca ), Robert Kantor, Kyoko Baba, Usman Erdosy, Keanre Eouanzoui & Mark James, Differences in written discourse in independent and integrated prototype tasks for next generation TOEFL. Assessing Writing (Amsterdam, the Netherlands) 10.1 (2005), 5–43. 05–318Eckes, Thomas (TestDaF Institute, the Netherlands), Melanie Ellis, Vita Kalnberzina, Karmen Piorn, Claude Springer, Krisztina Szollás & Constance Tsagari, Progress and problems in reforming public language examinations in Europe: cameos from the Baltic States, Greece, Hungary, Poland, Slovenia, France and Germany. Language Testing (London,UK) 22.3 (2005), 355–377. 05–319Figueras, Neus (Department of Education, Generalitat de Catalunya, Spain), Brian North, Sauli Takala, Norman Verhelst & Piet Van Avermaet, Relating examinations to the Common European Framework: a manual. Language Testing (London, UK) 22.3 (2005), 261–279. 05–320Green, Anthony (Cambridge ESOL Examinations, Cambridge, UK), EAP study recommendations and score gains on the IELTS Academic Writing test. Assessing Writing (Amsterdam, the Netherlands) 10.1 (2005), 44–60. 05–321Green, Rita & Dianne Wall (Lancaster U, UK), Language testing in the military: problems, politics and progress. Language Testing (London,UK) 22.3 (2005), 379–398. 05–322Hasselgreen, Angela (The U of Bergen, Norway), Assessing the language of young learners. Language Testing (London,UK) 22.3 (2005), 337–354. 05–323Klein, Joseph ( kleinj@mail.biu.ac.il ) & David Taub, The effect of variations in handwriting and print on evaluation of student essays. Assessing Writing (Amsterdam, the Netherlands) 10.2 (2005), 134–148. 05–324Little, David (Trinity College, Dublin, Ireland), The Common European Framework and the European Language Portfolio: involving learners and their judgements in the assessment process. Language Testing (London, UK) 22.3 (2005), 321–336. 05–325Lumley, Tom & Barry O'Sullivan (Australian Council for Educational Research, Australia), The effect of test-taker gender, audience and topic on task performance in tape-mediated assessment of speaking. Language Testing (London,UK) 22.4 (2005), 415–437. 05–326Luxia, Qi (Guandong U of Foreign Studies, China), Stakeholders' conflicting aims undermine the washback function of a high-stakes test. Language Testing (London, UK) 22.2 (2005), 142–173. 05–327Poehner, Matthew E. & James P. Lantolf (The Pennsylvania State U, USA), Dynamic assessment in the language classroom. Language Teaching Research (London, UK) 9.3 (2005), 233–265. 05–328Stansfield, Charles W. & William E. Hewitt (Second Language Testing Inc., USA), Examining the predictive validity of a screening test for court interpreters. Language Testing (London, UK) 22.4 (2005), 438–462. 05–329Trites, Latricia (Murray State U, USA) & Mary McGroarty, Reading to learn and reading to integrate: new tasks for reading comprehension tests?Language Testing (London, UK) 22.2 (2005), 174–210. 05–330Uiterwijk, Henny (Citogroep, Arnem, the Netherlands) & Ton Vallen, Linguistic sources of item bias for second generation immigrants in Dutch tests. Language Testing (London, UK) 22.2 (2005), 211–234. 05–331Weems, Gail H. (Arkansas Little Rock U, USA; ghweems@ualr.edu ), Anthony J. Onwuegbuzie & Daniel Lustig, Profiles of respondents who respond inconsistently to positively- and negatively-worded items on rating scales. Evaluation & Research in Education (Clevedon, UK) 17.1 (2003), 45–60. 05–332Weir, Cyril J. (Roehampton U, UK), Limitations of the Common European Framework for developing comparable examinations and tests. Language Testing (London, UK) 22.3 (2005), 281–300. 05–333Xi, Xiaoming (U of California, USA), Do visual chunks and planning impact performance on the graph description task in the SPEAK exam?Language Testing (London, UK) 22.4 (2005), 463–508.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.589 | 0.449 |
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".