Bibliographic record
Abstract
Using a lingua franca for scholarly communication offers some advantages, but it also limits research diversity, and there is a growing movement to encourage publication in other languages. Both approaches require scholars to access material through other languages, and more people are turning to machine translation to help with this task. Machine translation has improved considerably in recent years with the introduction of artificial intelligence techniques such as machine learning; however, it is far from perfect and users who are not trained as professional translators need to improve their machine translation literacy to use this technology effectively. Machine translation literacy is less about acquiring techno-procedural skills and more about developing cognitive competences. In this way, machine translation literacy aligns with the overall direction of the Association of College & Research Libraries’ (2015) Framework for Information Literacy for Higher Education, which encourages a conceptual, rather than a skills-based, approach. This case study presents a pilot project in which machine translation literacy instruction was incorporated into a broader program of information literacy and delivered to first-year students—both Anglophone and non-Anglophone—at a Canadian university. Students were surveyed and, overall, they found the machine translation literacy module to be valuable and recommended that similar instruction be made available to all students. Academic librarians are well positioned to participate in the delivery of machine translation literacy instruction as part of a broader information literacy program, and in so doing, they can promote linguistic diversity and better enable students and researchers from all regions to participate in scholarly conversations.
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 | 0.025 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.045 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".