Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
05–345 Byon, Andrew Sangpil (Albany U, USA; abyon@albany.edu ), Classroom assessment tools and students' affective stances: KFL classroom settings . Language and Education (Clevedon, UK) 19 .3 (2005), 173–193. 05–346 Costa, Jennifer, Gary McPhail, Janet Smith & Maria Estela Brisk (Boston College, USA), Faculty first – the challenge of infusing the teacher education curriculum with scholarship on English language learners . Journal of Teacher Education (Thousand Oaks, CA, USA) 56 .2 (2005), 104–118. 05–347 Gebhard, Meg (U of Massachusetts, Amherst, USA; gebhard@educ.umass.edu ), School reform, hybrid discourses, and second language literacies . TESOL Quarterly (Alexandria, VA, USA) 39 .2, 187–210. 05–348 Jessner, Ulrike (U of Innsbruck, Austria), Multilingual metalanguage, or the way multilinguals talk about their languages . Language Awareness (Clevedon, UK) 14 .1 (2005), 56–69. 05–349 Liebscher, Grit (Waterloo U, Canada; gliebsch@uwaterloo.ca ) & Jennifer Dailey-O'Cain , Learner code-switching in the content-based foreign language classroom . The Modern Language Journal (Malden, MA, USA) 89 .2 (2005), 234–247.
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.
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) | 0.006 | 0.001 |
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 it