Bibliographic record
Abstract
05–171 Cumming, Alister (Toronto U, Canada; acumming@oise.utoronto.ca ), Kantor, R., Baba, Kyoko, Erdosy, Usman, Eouanzoui, Keanre & James, Mark , Differences in written discourse in independent and integrated prototype tasks for next generation TOEFL . Assessing Writing (Amsterdam, the Netherlands) 10 .1 (2005), 5–43. 05–172 Green, Anthony (Cambridge ESOL Examinations, Cambridge, UK; green.a@ucles.org ), EAP study recommendations and score gains on the IELTS Academic Writing test . Assessing Writing (Amsterdam, the Netherlands) 10 .1 (2005), 44–60. 05–173 Harwood, Nigel (U of Essex, UK; nharwood@essex.ac.uk ), ‘I hoped to counteract the memory problem, but I made no impact whatsoever’: discussing methods in computing science using I . English for Specific Purposes (Amsterdam, the Netherlands) 24 .3 (2005), 243–267. 05–174 Kanoksilapatham, Budsaba (Silpakorn U, Thailand; kanoksib@georgetown.edu ), Rhetorical structure of biochemistry research articles . English for Specific Purposes (Amsterdam, the Netherlands) 24 .3 (2005), 269–292. 05–175 Sharp, Alastair (Lingnan U, Hong Kong, China; alastair@in.edu.hk ), Strategies and predilections in reading expository text: the importance of text patterns . RELC Journal (Thousand Oaks, CA, USA) 35 .3 (2004), 329–349. 05–176 Stapleton, P. (Hokkaido U, Japan), Evaluating web-sources: Internet literacy and L2 academic writing . ELT Journal (Oxford, UK) 59 .2 (2005), 135–143. 05–177 Waring, H. (Teachers College, Columbia U, USA), Peer tutoring in a graduate writing centre: identity, expertise, and advice resisting . Applied Linguistics (Oxford, UK) 26 .2 (2005), 141–168.
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 | 0.000 |
| 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".