‘TEEB Begins Now’: A Virtual Moment in the Production of Natural Capital
Bibliographic record
Abstract
This article uses theories of virtualism to analyse the role of The Economics of Ecosystems and Biodiversity (TEEB) project in the production of natural capital. Presented at the 10th Conference of the Parties to the Convention on Biological Diversity, the project seeks to redress the ‘economic invisibility of nature’ by quantifying the value of ecosystems and biodiversity. This endeavour to put an economic value on ecosystems makes nature legible by abstracting it from social and ecological contexts and making it subject to, and productive of, new market devices. In reducing the complexity of ecological dynamics to idealized categories TEEB is driven by economic ideas and idealism, and, in claiming to be a quantitative force for morality, is engaged in the production of practices designed to conform the ‘real’ to the virtual. By rendering a ‘valued’ nature legible for key audiences, TEEB has mobilized a critical mass of support including modellers, policy makers and bankers. We argue that TEEB's rhetoric of crisis and value aligns capitalism with a new kind of ecological modernization in which ‘the market’ and market devices serve as key mechanisms to conform the real and the virtual. Using the case of TEEB, and drawing on data collected at COP10, we illustrate the importance of international meetings as key points where idealized models of biodiversity protection emerge, circulate and are negotiated, and as sites where actors are aligned and articulated with these idealized models in ways that begin further processes of conforming the real with the virtual and the realization of ‘natural capital’.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".