Bibliographic record
Abstract
05–314 Alderson, 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. 05–315 Al-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. 05–316 Broadfoot, 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. 05–317 Cumming, 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. 05–318 Eckes, 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. 05–319 Figueras, 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. 05–320 Green, 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. 05–321 Green, Rita & Dianne Wall (Lancaster U, UK), Language testing in the military: problems, politics and progress . Language Testing (London,UK) 22 .3 (2005), 379–398. 05–322 Hasselgreen, Angela (The U of Bergen, Norway), Assessing the language of young learners . Language Testing (London,UK) 22 .3 (2005), 337–354. 05–323 Klein, 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. 05–324 Little, 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. 05–325 Lumley, 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. 05–326 Luxia, 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. 05–327 Poehner, 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. 05–328 Stansfield, 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. 05–329 Trites, 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. 05–330 Uiterwijk, 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. 05–331 Weems, 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. 05–332 Weir, 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. 05–333 Xi, 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".