Calculative Practices in International Environmental Governance: In (Partial) Defence of Indicators
Bibliographic record
Abstract
Abstract The role of calculative practices such as goals and indicators in international environmental governance causes concern among many observers, who view them as promoting a reductivist approach to the non-human world and privileging economic understandings of environmental governance above all others. Yet they possess enormous potential to provide insights into the non-human world that could be of great benefit to governance. This article takes seriously critical perspectives of calculative practices, while exploring a weakness in much of the critical literature, namely a failure to examine assumptions about the nature of scientific knowledge and the manner in which it is, and ought to be, taken up by policy makers. I contend that both the design of environmental regimes and critical analyses of these regimes bear the marks of the influence, albeit indirect, of early 20thcentury views on the superiority of scientific knowledge and its unique capacity to ground decision making. I argue that a richer, more nuanced account of the co-production of ecological metrics such as goals and indicators and their potential contributions to ecosystem governance and sustainability is necessary. With such accounts, scholars and political authorities would be in a better position to address the very real pitfalls and dangers of calculative practices while not feeling compelled to forego these potentially powerful approaches.
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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 | 0.029 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.072 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".