Numerically Bounded Linguistic Probability Schemes Are Unlikely to Communicate Uncertainty Effectively
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In a recent issue of Earth's Future (vol. 7, pp. 1020–1026), S. C. Lewis et al. (2019, https://doi.org/10.1029/2019EF001273 ) recommended a numerically bounded linguistic probability (NBLP) scheme for communicating probabilistic information in extreme event attribution studies. We provide a critique of NBLP schemes in general and of Lewis et al.'s in particular, noting two key points. First, evidence from voluminous behavioral science research on the interpretation of linguistic probabilities indicates that NBLP schemes are an ineffective means of communicating uncertainty to others. Second, where the motivation to implement such schemes nevertheless persists, the schemes should be developed through an evidence‐based approach that seeks to optimize interpretational agreement between the scheme and users.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it