Exclusion of the non-English-speaking world from the scientific literature: Recommendations for change for addiction journals and publishers
Bibliographic record
Abstract
Background: While English is only the native language of 7.3% of the world's population and less than 20% can speak the language, nearly 75% of all scientific publications are English. Aim: To describe how and why scientific contributions from the non-English-speaking world have been excluded from addiction literature, and put forward suggestions for making this literature more accessible to the non-English-speaking population. Methods: A working group of the International Society of Addiction Journal Editors (ISAJE) conducted an iterative review of issues related to scientific publishing from the non-English-speaking world. Findings: We discuss several issues stemming from the predominance of English in the scientific addiction literature, including historical drivers, why this matters, and proposed solutions, focusing on the increased availability of translation services. Conclusion: The addition of non-English-speaking authors, editorial team members, and journals will increase the value, impact, and transparency of research findings and increase the accountability and inclusivity of scientific publications.
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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 | 0.493 | 0.723 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.055 | 0.056 |
| Open science | 0.013 | 0.018 |
| Research integrity | 0.028 | 0.031 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".