Including <scp>non‐English</scp> language articles in systematic reviews: A reflection on processes for identifying low‐cost sources of translation support
Bibliographic record
Abstract
Non-English language (NEL) articles are commonly excluded from published systematic reviews. The high cost associated with professional translation services and associated time commitment are often cited as barriers. Whilst there is debate as to the impact of excluding such articles from systematic reviews, doing so can introduce various biases. In order to encourage researchers to consider including these articles in future reviews, this paper aims to reflect on the experience and process of conducting a systematic review which included NEL articles. It provides an overview of the different approaches used to identify sources of low-cost translation support and considers the relative merits of, among others, seeking support through universities, social media, word-of-mouth, and use of personal contacts.
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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 | 0.775 | 0.903 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.018 | 0.026 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.035 | 0.039 |
| Open science | 0.011 | 0.023 |
| Research integrity | 0.020 | 0.032 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".