When Half Is at Least 50%: Effect of “Framing” and Probability Level on Frequency Estimates
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 Expert judgment often involves estimating magnitudes, such as the frequency of deaths due to a pandemic. Three experiments ( N s = 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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