Bibliographic record
Abstract
07–604 Abbott, Marilyn (Alberta Education, Canada; marilyn.abbott@gov.ab.ca ), A confirmatory approach to differential item functioning on an ESL reading assessment . Language Testing (Sage) 24.1 (2007), 7–36. 07–605 Barber, Richard (Dubai Women's College, UAE), A practical model for creating efficient in-house placement tests . The Language Teacher (Japan Association for Language Teaching) 31.2 (2007), 3–7. 07–606 Cheng, Liying , Don Klinger & Ying Zheng (Queen's U, Canada; chengl@edu.queensu.ca ), The challenges of the Ontario Secondary School Literacy Test for second language students . Language Testing (Sage) 24.2 (2007), 185–208. 07–607 Cohen, Andrew (U Minnesota, USA) & Thomas Upton , ‘I want to go back to the text’: Response strategies on the reading subtest of the new TOEFL® . Language Testing (Sage) 24.2 (2007), 209–250. 07–608 Dávid, Gergely (Eötvös Loránd U, Hungary; david.soproni@t-online.hu ), Investigating the performance of alternative types of grammar items . Language Testing (Sage) 24.1 (2007), 65–97. 07–609 Elder, Catherine (U Melbourne, Australia; caelder@unimelb.edu.au ), Gary Barkhuizen , Ute Knoch & Janet Von Randow , Evaluating rater responses to an online training program for L2 writing assessment . Language Testing (Sage) 24.1 (2007), 37–64. 07–610 Qian, David (The Hong Kong Polytechnic U, China; David.Qian@polyu.edu.hk ), Assessing university students: Searching for an English language exit test . RELC Journal (Sage) 38.1 (2007), 18–37. 07–611 Scott Walters, Francis (U New York, USA; Francis.Walters@qc.cuny.edu ), A conversation-analytic hermeneutic rating protocol to assess L2 oral pragmatic competence . Language Testing (Sage) 24.2 (2007), 155–183. 07–612 Shiotsu, Toshihiko (Kurume U, Japan; toshihiko_shiotsu@kurume-u.ac.jp ) & Cyril Weir , The relative significance of syntactic knowledge and vocabulary breadth in the prediction of reading comprehension test performance . Language Testing (Sage) 24.1 (2007), 99–128. 07–613 Vanderveen, Terry (Kangawa U, Japan), The effect of EFL students' self-monitoring on class achievement test scores . JALT Journal (Japan Association for Language Teaching) 28.2 (2006), 197–206. 07–614 Xi, Xiaoming (Educational Testing Service, USA; xxi@ets.org ), Evaluating analytic scoring for the TOEFL® Academic Speaking Test (TAST) for operational use . Language Testing (Sage) 24.2 (2007), 251–286.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".