When Half Is at Least 50%: Effect of “Framing” and Probability Level on Frequency Estimates
Bibliographic record
Abstract
ABSTRACT Expert judgment often involves estimating magnitudes, such as the frequency of deaths due to a pandemic. Three experiments (Ns = 902, 431, and 755, respectively) were conducted to examine the effect of outcome framing (e.g., half of a threatened group expected to survive vs. die), probability level (low vs. high), and probability format (verbal, numeric, or combined) on the estimated frequency of survivals/deaths. Each experiment found an interactive effect of frame and probability level, which supported the hypothesis that forecasted outcomes received by participants were implicitly quantified as lower bounds (i.e., “at least half”). Responding in a manner consistent with a lower‐bound “at least” interpretation was unrelated to incoherence (Experiments 1 and 2) and positively related to numeracy (Experiments 1 and 3), verbal reasoning (Experiment 3), and actively open‐minded thinking (Experiments 2 and 3). The correlational results indicate that implicit lower bounding is an aspect of linguistic inference and not a cognitive error. Implications for research on framing effects are discussed.
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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 | 0.017 | 0.181 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".