Promoting multilingualism and bibliodiversity by enhancing discoverability
Bibliographic record
Abstract
Since the launch of the Helsinki Initiative on Multilingualism in Scholarly Communication, in 2019, we have seen an increasing understanding of the equity and diversity aspects of languages in science. Still, language inequities continue to characterize the scholarly ecosystem in favor of English and journals with a high impact factor (JIF), and this trend is global (Larivière, Siler, 2022). As the fifth most spoken language in the world, the status of the French language in the sciences, particularly in the fields of humanities and social sciences, appears to be less critical than that of languages spoken by a smaller number of speakers. While this observation may initially reassure about the ability of French speakers to publish their research in their native language, the relative strength of the French language in this regard is not consistent worldwide. Canadian whose mother tongue or working language is French feel much greater linguistic pressure than their counterparts in France, as they account for just over 20% of the 40 million Canadians. What’s more, almost 44% of French-speaking Canadian researchers had never published a research article in French – 71% in the technical sciences and medicine, and 35% in the humanities and social sciences (Forgues, 2021). In response to these concerning statistics, the Ministère de l’Enseignement supérieur du Québec, in collaboration with the Délégation Générale de la Langue Française, is launching a three-year project to explore ways of increasing the discoverability of French-language research. The purpose of this proposal is to outline the primary objectives of the project, led by Vincent Larivière under the auspices of the UNESCO Chair in Open Science. It aims to gather feedback from the OPERAS community and foster the exchange of perspectives and approaches regarding bibliodiversity and multilingualism.
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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 | 0.040 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.035 | 0.033 |
| Open science | 0.003 | 0.050 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".