When Does Framing Influence Preferences, Risk Perceptions, and Risk Attitudes? The Explicated Valence Account
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 When faced with an expected loss and a choice between a sure option and a risky option, the gain–loss framing of the problem has been shown to influence option preference. According to prospect theory, this framing effect is the result of contradictory attitudes about risks involving gains and losses. This article develops and tests an alternative explicated valence account (EVA), which proposes that preference reversals are caused by differences in the explicated outcome valences of the options under consideration. EVA can account for previous findings where framing effects are observed, eliminated, or even reversed. In two experiments, EVA successfully predicted when framing effects were observed, eliminated, and reversed. The findings also showed that although framing influenced risk perception, it did not influence risk attitudes. Copyright © 2015 Her Majesty the Queen in Right of Canada Journal of Behavioral Decision Making © 2015 John Wiley & Sons, Ltd.
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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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