Bibliographic record
Abstract
05–81Aguado, Karin (U of Bielefeld, Germany). Evaluation fremdsprachlicher Wortschatz-kompetenz: Funktionen, Prinzipien, Charakteristika, Desiderate [Evolution of foreign vocabulary competence: functions, principles, characteristics and deficits]. Fremdsprachen Lehren und Lernen (Tübingen, Germany) 33 (2004), 231–250. 05–82Hansen, Eric, G. (ETS, Princeton, USA; ehansen@ets.org ), Mislevy, Robert, J., Steinberg, Linda, S., Lee, Moon, J. & Forer, Douglas, C. Accessibility of tests for individuals with disabilities within a validity framework. System (Oxford, UK) 33.1 (2005), 107–133. 05–83Ingram, David (Melbourne U Private, Australia; d.ingram@muprivate.edu.au ). Towards more authenticity in language testing. Babel-Journal of the AFMLTA (Queensland, Australia) 39.2(2004), 16–24. 05–84Latham, Dorothy (Independent Education Consultant, UK). Speaking, listening and learning: a rationale for the Speaking and Listening Profile. English in Education (Sheffield, UK) 39.1 (2005)60–74. 05–85Spratt, Mary (Cambridge, UK). Washback and the classroom: the implications for teaching and learning of studies of washback from exams. Language Teaching Research (London, UK) 9.1 (2005), 5–29. 05–86Suzuki, Manami & Daza, Carolina (OISE, Toronto, Canada; msusuki@oise.utoronto.ca ). A review of the reading section of the TOEIC. TESL Canada Journal (Burnaby, Canada) 22.1 (2004), 16–24. 05–87Weaver, Chris & Romanko. Rick (Tokyo U of Agriculture and Technology, Japan). Assessing oral communication competence in a university entrance examination. The Language Teacher (Kyoto, Japan) 29.1 (2005), 3–9.
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 | 0.004 | 0.013 |
| 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 | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.353 | 0.239 |
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".