Improving the communication of uncertainty in climate science and intelligence analysis
Bibliographic record
Abstract
Public policymakers routinely receive and communicate information characterized by uncertainty. Decisions based on such information can have important consequences, so it is imperative that uncertainties are communicated effectively. Many organizations have developed dictionaries, or lexicons, that contain specific language (e.g., very likely, almost certain) to express uncertainty. But these lexicons vary greatly and only a few have been empirically tested. We have developed evidence-based methods to standardize the language of uncertainty so that it has clear meaning understood by all parties in a given communication. We tested these methods in two policy-relevant domains: climate science and intelligence analysis. In both, evidence-based lexicons were better understood than those now used by the Intergovernmental Panel on Climate Change, the U.S. National Intelligence Council, and the U.K. Defence Intelligence. A well-established behavioral science method for eliciting the terms’ full meaning was especially effective for deriving such lexicons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".