Bibliographic record
Abstract
04–83Akiyama, Tomoyasu (U. Melbourne, Australia). Assessing speaking: issues in school-based assessment and the introduction of speaking tests into the Japanese senior high school entrance examination. JALT Journal (Tokyo, Japan), 25, 2 (2003), 117–141. 04–84Chiang, Steve (Yuan Ze University, Taiwan). The importance of cohesive conditions to perceptions of writing quality at the early stages of foreign language learning. System (Oxford, UK), 31 (2003), 471–484. 04–85Escamilla, Kathy, Mahon, Elizabeth, Riley-Bernal, Heather and Rutledge, David (U. of Colorado, Boulder, USA). High-stakes testing, Latinos, and English language learners: lessons from Colorado. Bilingual Research Journal (Arizona, USA), 27, 1 (2003), 25–49. 04–86Gorsuch, Greta (Texas Tech U., USA; Email: greta.gorsuch@ttu.edu ). Test takers' experiences with computer-administered listening comprehension tests: interviewing for qualitative explorations of test validity. Calico Journal (Texas, USA), 21, 2 (2004), 339–371. 04–87Hardcastle, Peter.How to not test language (Part 2). Language Testing Update (Lancaster, UK), 33 (2003), 28–35. 04–88Hemard, D. and Cushion, S. (London Metropolitan, University, UK; Email: hemard@lgu.ac.uk ). Design and evaluation of an online test: assessment conceived as a complementary CALL tool. Computer Assisted Language Learning (Lisse, The Netherlands), 16, 2–3 (2003), 119–139. 04–89Ishii, David N. and Baba, Kyoko (U. of Toronto, Canada; Email: dishii@aoise.utoronto.ca ). Locally developed oral skills evaluation in ESL/EFL classrooms: a checklist for developing meaningful assessment procedures. TESL Canada Journal/Revue TESL du Canada (Burnaby, Canada), 21, 1 (2003), 79–96. 04–90Iwashita, Noriko and Grove, Elizabeth (University of Melbourne, Australia). A comparison of analytic and holistic scales in the context of a specific-purpose speaking test. Prospect (Sydney, Australia), 18, 3 (2003), 25–35. 04–91Lee, Yong-Won (Educational Testing Service, Princeton, NJ, US; Email: ylee@ets.org ). Examining passage-related local item dependence (LID) and measurement construct using Q3statistics in an EFL reading comprehension test. Language Testing (London, UK), 21, 1 (2004), 74–100. 04–92Qian, David D. (Hong Kong Polytechnic U., Hong Kong; Email: David.Qian@polyu.edu.hk ) and Schedl, Mary (Educational Testing Service, Princeton, NJ, US). Evaluation of an in-depth vocabulary knowledge measure for assessing reading performance. Language Testing (London, UK), 21, 1 (2004), 28–52. 04–93Rea-Dickins, Pauline (University of Bristol, UK). Classroom assessment of English as an additonal language: Key stage 1 contexts – summary of research findings. Language Testing Update (Lancaster, UK), 33 (2003), 48–53. 04–94Rodgers, Catherine, Meara, Paul and Jacobs, Gabriel (U. of Wales Swansea, UK). Factors affecting the standardisation of translation examinations. Language Learning Journal (London, UK), 28 (Winter 2003), 49–54.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.321 | 0.236 |
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".