The role of non-English-language science in informing national biodiversity assessments
Bibliographic record
Abstract
Consulting the best available evidence is key to successful conservation decision-making. While much scientific evidence on conservation continues to be published in non-English languages, a poor understanding of how non-English-language science contributes to conservation decision-making is causing global assessments and studies to practically ignore non-English-language literature. By investigating the use of scientific literature in biodiversity assessment reports across 37 countries/territories, we have uncovered the established role of non-English-language literature as a major source of information locally. On average, non-English-language literature constituted 65% of the references cited, and these were recognized as relevant knowledge sources by 75% of report authors. This means that by ignoring non-English-language science, international assessments may overlook important information on local and/or regional biodiversity. Furthermore, a quarter of the authors acknowledged the struggles of understanding English-language literature. This points to the need to aid the use of English-language literature in domestic decision-making, for example, by providing non-English-language abstracts or improving and/or implementing machine translation. (This abstract is also avaialble in 21 other languages in Supplementary Data 4)
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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 | 0.271 | 0.514 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.045 | 0.031 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".