Bibliographic record
Abstract
As someone who works with international teaching assistants (ITAs) in the USA, I found this collected volume to be at turns thought-provoking, useful, and perplexing. ITAs are Chinese, Indian, Korean, etc. graduate students in the sciences and humanities who come to earn graduate degrees in Canada and the USA. In order to be supported as graduate students, they work as class and lab instructors to North American undergraduates. The kinds of intensive, massed language instruction (and the proposed resulting psycholinguistic pro-cesses) the various authors of Intensive Exposure Experiences in Second Language Learning describe in immersion programs in Canadian schools and other set-tings are eerily apt when considering ITAs. ITAs in this context should be seen as high intermediate adult learners of English as a second language. Perhaps, for the first time in their second language learning lives, ITAs get intensive L2 instruction. This happens upon arrival in North America as part of L2-profi-ciency-for-teaching requirements of a given university. Presumably, the issues of practice (as defined by DeKeyser 2007), explicit instruction, and massed and
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.347 | 0.296 |
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".