‘Measurementality’ in Biodiversity Governance: Knowledge, Transparency, and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (Ipbes)
Bibliographic record
Abstract
Current policies and practices in biodiversity conservation have been increasingly influenced by neoliberal approaches since the 1990s. The authors focus on the principle of transparency as a self-proclaimed basis of neoliberal environmental governance, and on the role of standardized science-based measurements which it purportedly affords. The authors introduce the term ‘measurementality’ to signify the governance logic that emerges when transparency comes to stand next to effectiveness and efficiency as neoliberal principles and to highlight the connections that are forged between economic, managerial, and technocratic discourses. The example of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) is used to discuss the role of measurementality in global biodiversity governance. The analysis suggests that IPBES aims to coordinate the science–policy interface in order to optimize the generation of user-friendly knowledge of those elements of biodiversity that are considered politically and economically relevant: At the current economic juncture, these being in essence ecosystem services. Based on these findings, the authors proceed by critically reflecting on the ways in which the measurementality logic of IPBES may not only result in an impoverishment of the biodiversity research agenda, but also in an impoverished understanding of biodiversity itself. To conclude, the authors argue that measurementality is part and parcel of the neoliberal paradigm in which science produces the raw materials for subsequent control and exchange and that, as a result, the intersection of science, discourse, policy, and economics within these governance systems requires sustained critical scrutiny.
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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 | 0.037 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.078 |
| Scholarly communication | 0.029 | 0.031 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".