DISCLOSURE-BASED GOVERNANCE FOR CLIMATE ENGINEERING RESEARCH
Bibliographic record
Abstract
Transparency has become a dominant theme within academic and policy discussions on climate engineering (CE) research governance. As CE research moves from modelling and laboratory studies to field experiments, there is a need to operationalize transparency; that is, to move from transparency in principle to transparency in practice. This, in turn, requires greater attention be paid to the purposes that CE research transparency is intended to serve since the ends sought, as well as the context in which they will operate, will drive the design features of disclosure mechanisms. The objective of this paper is to focus attention on the implementation challenges that disclosure faces in the realm of CE research governance. To this end, we identify and elaborate on two distinct roles that disclosure-based governance is anticipated to play: minimization of the environmental and social risks associated with CE research; and to generate and maintain legitimacy in the research process itself. Drawing on that discussion, we then identify a number of key design features that disclosure-based governance will need to achieve those ends, and we argue in favour of an approach to disclosure-based governance that recognizes the iterative and inherently normative nature of CE governance and supports the development of a decentralized system of disclosure serving multiple ends.
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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 | 0.162 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.011 | 0.009 |
| 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".