Machine translation literacy instruction for international business students and business English instructors
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
As the number of non-Anglophone students studying business through the medium of English continues to increase, there is a growing interest in the potential of machine translation for helping these students with English-language writing. Language instructors recognize the futility of trying to ban the use of such tools, but they are apprehensive about their use. Academic librarians already deliver various forms of digital literacy instruction, and this article describes the design and delivery of a machine translation literacy workshop for international business students and their language instructors. Feedback was largely positive, but it may be helpful to customize future workshops for specific language groups. The target audience could also be expanded to include non-Anglophone faculty as well as students since the former are under increasing pressure to publish in English. The overall experience points to the benefit of collaboration between librarians and other experts in order to adapt to the changing needs of the campus community and to offer meaningful services and support in this period of rapid change.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it