ENABLING PERFORMATIVITY IN 'SKUNK LABS': THE UNTOLD STORY OF CARBON MARKETS DESIGN
Bibliographic record
Abstract
Climate change has become in 20 years one of the greatest economic, environmental and social challenges of our modern society. A wide variety of organizations – NGOs, governments, business, international bodies, local communities, research think tanks – are working together to design and implement a low carbon society. In this particularly uncertain context, characterized by distributed, lacunar, messy and sometimes contradictory scientific knowledge, the actors fail to converge on a common project regarding the architecture of a low carbon society. Projects and visions vary among actors and over time. Nevertheless, it is commonly admitted among experts and economists that a carbon price that would be stable, predictable and fair could provide the long term coordination that is needed to drive the implementation of a low carbon society. "A price of carbon would solve any problem" said a French expert in a recent interview . A ‘right' price of carbon would diffuse in the economy and provide long term drive for technology breakthroughs and switch to low carbon products said another one . Such a ‘right' carbon price would then stir up the profound societal changes that are required. In Europe, these great expectations over a ‘right' carbon price have aroused an on-going design activity that enables the existence of the European carbon market (EU-ETS). The ‘official' story of how carbon markets were designed and implemented, as it is told in economic handbooks and in the press, is well known and widely documented (e.g. Braun, 2009; Ellerman & al, 2010; Hourcade, 2002; Cass, 2005; Wetestad, 2005). According to this story, environmental economics is supposed to be particularly performative as it presents carbon markets as the output of thirty years of research program in environmental economics initiated in 1960 by Ronald Coase and his famous article, ‘the problem of social costs'. This common representation tends to overlook three activities that enabled the concrete performation of theoretical economy; that is to say design, negotiation and revision. We propose to adopt the perspective of (Callon 2009): "How are the different knowledge and know-how transported, experience capitalized on, and evaluations conducted?" We claim in this paper that in the case of carbon markets, the existence of design spaces that mediate between economics and economy (Guala, 2007) is central to explain the performation of the EU-ETS.
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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 | 0.026 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.078 |
| Scholarly communication | 0.023 | 0.031 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".