Understanding the dynamic nature of risk in climate change assessments—A new starting point for discussion
Bibliographic record
Abstract
Abstract This article sets out the current conceptualisation and description of risk used by the Intergovernmental Panel on Climate Change (IPCC). It identifies limitations in capacity to reflect the dynamic nature of risk components, and the need for standardisation and refinement of methods used to quantify evolving risk patterns. Recent studies highlight the changing nature of hazards, exposure and vulnerability, the three components of risk, and demonstrate the need for coordinated guidance on strategies and methods that better reflect the dynamic nature of the components themselves, and their interaction. Here, we discuss limitations of a static risk framework and call for a way forward that will allow for a better understanding and description of risk. Such advancements in conceptualisation are needed to bring closer the understanding and description of risk in theory with how risk is quantified and communicated in practice. To stimulate discussion, this article proposes a formulation of risk that clearly recognises the temporally evolving nature of risk components.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →1 of 3 models called this metaresearch. This work is contested: it sits on the field's empirical boundary, and whether it counts depends on which model you asked. It is one of the 51 works in the disagreement dossier.
Critique of the risk framework the IPCC uses in climate assessments, calling for standardization of the methods used to quantify risk; borderline between assessment methodology and domain climate-risk theory.
It develops a conceptual framework for climate risk rather than studying research practice.
Conceptual paper on IPCC climate-risk frameworks, not metaresearch on how research is done or evaluated.
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 | 0.041 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.040 |
| Scholarly communication | 0.018 | 0.042 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 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".