Facilitating sender-receiver agreement in communicated probabilities: Is it best to use words, numbers or both?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Organizations tasked with communicating expert judgments couched in uncertainty often use numerically bounded linguistic probability schemes to standardize the meaning of verbal probabilities. An experiment ( N = 1,202) was conducted to ascertain whether agreement with such a scheme was better when probabilities were presented verbally, numerically or in a combined “verbal + numeric” format. Across three agreement measures, the numeric and combined formats outperformed the verbal format and also yielded better discrimination between low and high probabilities and were less susceptible to the fifty-fifty blip phenomenon. The combined format did not confer any advantage over the purely numeric format. The findings indicate that numerically bounded linguistic probability schemes are an ineffective means of communicating information about probabilities to others and they call into question recommendations for use of the combined format for delivering such schemes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it