Machine translation in higher education: Perceptions, policy, and pedagogy
Bibliographic record
Abstract
Multiple studies have shown that language learners and other students undertaking postsecondary studies in an additional language (L2) consult digital translation tools to complete course‐related work despite general disapproval of their use by instructors. Significant improvements in the accuracy of machine translation (MT) along with their widespread use among students present ethical and pedagogical implications that have yet to be coherently addressed by instructors and institutions at the tertiary level. Recognizing MT as inextricable from L2 users' academic realities, this article reviews the current research on perceptions and purposes of its use in higher education institutions, discusses MT at the policy level (e.g., gaps in legislation related to academic integrity and, more broadly, inconsistencies between the aims of internationalization and the continued delegitimization of marginalized varieties of English), outlines various ways that MT can be harnessed to support learning (e.g., for vocabulary acquisition, writing, metalinguistic awareness, learner autonomy), and suggests ways forward in education, research, and theory on the intersection of MT and learning.
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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".