Does language matter? A case study of epidemiological and public health journals, databases and professional education in French, German and Italian
Bibliographic record
Abstract
Epidemiology and public health are usually context-specific. Journals published in different languages and countries play a role both as sources of data and as channels through which evidence is incorporated into local public health practice. Databases in these languages facilitate access to relevant journals, and professional education in these languages facilitates the growth of native expertise in epidemiology and public health. However, as English has become the lingua franca of scientific communication in the era of globalisation, many journals published in non-English languages face the difficult dilemma of either switching to English and competing internationally, or sticking to the native tongue and having a restricted circulation among a local readership. This paper discusses the historical development of epidemiology and the current scene of epidemiological and public health journals, databases and professional education in three Western European languages: French, German and Italian, and examines the dynamics and struggles they have today.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".