Bibliographic record
Abstract
International teaching assistants (ITAs) are Indian, Chinese, Korean, Turkish, etc. international students who have been admitted to graduate study at universities in the U.S.A. and Canada, and are being supported as instructors of undergraduate-level classes and labs in biology, chemistry, physics, and math. For the past 30 years, the number of ITAs has been increasing, and many departments at universities have come to rely largely on ITAs to cover their undergraduate teaching needs. As high-intermediate and low-advanced second language learners who must use their second language for professional purposes, ITAs face linguistic, social, professional, and cultural challenges. This is a learner population that deserves more attention, as I hope to establish here with this presentation of six research tasks. I have organized proposed research projects in such a way as to increase readers’ familiarity with this little publicized field, and also to relate the projects to different contexts of inquiry. By ‘contexts’ I mean ‘who is asking what and for what reasons.’ The two contexts of inquiry are: (1) Established areas of ITA program concern, including acquisition of fluency, prosody, and vocabulary; and (2) Working with ‘outside’ theories, such as the Output Hypothesis, and deliberate practice theory.
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 | 0.051 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.024 | 0.042 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".