Varieties of approaches to constructing physical climate storylines: A review
Bibliographic record
Abstract
Abstract The physical climate storyline (PCS) approach is increasingly recognized by the physical climate research community as a tool to produce and communicate decision‐relevant climate risk information. While PCS is generally understood as a single concept, different varieties of the approach are applied according to the aims and purposes of the PCS and the scientists that build them. To unpack this diversity of detail, this article gives an overview of key practices and assumptions of the PCS approach as developed by physical climate scientists, as well as their ties to similar approaches developed by the broader climate risk and adaptation research community. We first examine varieties of PCSs according to the length of the causal chain they explore, and the type of evidence used. We then describe how they incorporate counterfactual elements and the temporal perspective. Finally, we examine how value judgments are implicitly or explicitly included in the aims and construction of PCSs. We conclude the discussion by suggesting that the PCS approach can further mature in the way it incorporates the narrative element, in the way it incorporates value judgments, and in the way that the evidence chosen to build PCSs constrains what is considered plausible. This article is categorized under: Assessing Impacts of Climate Change > Scenario Development and Application Climate, History, Society, Culture > Technological Aspects and Ideas Paleoclimates and Current Trends > Modern Climate Change
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".