Geographic and epistemic pluralism in the sources of evidence informing international environmental science–policy platforms: lessons learnt from the IPBES values assessment
Bibliographic record
Abstract
Abstract Non-technical summary This article examines the challenges and opportunities to integrate diverse sources of evidence in assessments produced by international platforms working at the science–policy interface. Diversity (or pluralism) of sources of literature, both in terms of their geographic origin and disciplinary focus, is essential for assessments to inform decision-making across social–ecological contexts. Using the recently completed ‘Methodological Assessment of the Diverse Values and Valuation of Nature’ of the Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services as a case, we find that significant effort has been dedicated to reviewing diverse literature. We discuss three strategies to expand pluralism in future assessments. Technical summary Representing plural views in science–policy platforms is essential to avoid reproducing geographic and epistemic biases that permeate contemporary scientific knowledge production and synthesis. The Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services (IPBES) has strived to produce assessments that incorporate information from diverse regions and knowledge systems. We explore the geographic and epistemic pluralism of the literature included in the ‘Methodological Assessment of the Diverse Values and Valuation of Nature’ (VA), and the challenges and opportunities to achieve such knowledge pluralism. We applied a bibliometric analysis to the sources of evidence cited in the VA, and reflected on the assessment development process, in which we were directly involved. Our results highlight the success of different strategies developed by VA experts to engage with diverse sources of literature. Still, most evidence was English-language academic literature produced in Western Europe, Canada, and the United States, echoing the prominence of this literature in scientific publication in environmental disciplines. Reflecting on our experiences, we discuss strategies that could further enhance the geographic and epistemic pluralism in the information reviewed for future environmental assessments produced by IPBES and other international science–policy platforms. Social media summary Epistemic and geographic pluralism was partially achieved in IPBES Values Assessment, and can be further enhanced in future assessments.
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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 | 0.248 | 0.409 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.048 | 0.042 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.040 | 0.046 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".