Bibliographic record
Abstract
03—343 Boyd, Kenneth and Davies, Alan (U. of Edinburgh, UK; Email: a.davies@ed.ac.uk ). Doctors' orders for language testers: The origin and purpose of ethical codes. Language Testing (London, UK), 19, 3 (2002), 296—322. 03—344 Finch, Andrew Edward (Kyungpook National U., Korea; Email: aef@knu.ac.kr ). Ethical assessment: Implications for EFL educators in Korea. English Teaching (Korea), 57, 3 (2002), 333—51. 03—345 Huibregtse, Ineke (Utrecht U., The Netherlands; Email: i.huibregtse@ivlos.uu.nl ), Admiraal, Wilfried and Meara, Paul. Scores on a yes-no vocabulary test: Correction for guessing and response style. Language Testing (London, UK), 19, 3 (2002), 227—45. 03—346 Lumley, Tom (Hong Kong Polytechnic U.; Email: egluml@polyu.edu.hk ). Assessment criteria in a large-scale writing test: What do they really mean to the raters? Language Testing (London, UK), 19, 3 (2002), 246—76. 03—347 O'Sullivan, Barry (U. of Reading, UK; Email: b.e.osullivan@reading.ac.uk ). Learner acquaintanceship and oral proficiency test pair-task performance. Language Testing (London, UK), 19, 3 (2002), 277—95. 03—348 Qian, David D. (Hong Kong Polytechnic U.; Email: David.Qian@polyu.edu.hk ). Investigating the relationship between vocabulary knowledge and academic reading performance: An assessment perspective. Language Learning (Malden, MA, USA), 52, 3 (2002), 513—36. 03—349 Saif, Shahrzad (Université Laval, Canada; Email: shahrzad.saif@lli.ulaval.ca ). A needs-based approach to the evaluation of the spoken language ability of international teaching assistants. Canadian Journal of Applied Linguistics (Ottawa, Canada), 5, 1/2 (2002), 145—67. 03—350 Wolter, Brent (Hokkaido U., Japan; Email: wolter@ilcs.hokudai.ac.jp ). Assessing proficiency through word associations: Is there still hope? System (Oxford, UK), 30, 3 (2002), 315—29.
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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.457 | 0.333 |
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".