The Value of Investors Being in a Deliberative Mindset When Reading News Later Revealed to Be Fake
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 Investors face a difficult challenge in determining whether news they read is true or fake and, according to psychology theory, an additional challenge of ceasing to rely on news subsequently revealed to be fake. To help address this latter challenge, we examine whether prompting investors to be in a deliberative mindset reduces their reliance on news after they learn that it is fake without affecting their reliance on news later revealed to be true. Consistent with theory, investors adjust their valuation assessments when news is later revealed to be fake, and this adjustment is magnified for investors in a deliberative mindset. Importantly, our results reveal that a deliberative mindset does not cause investors to discount news later revealed to be true. Data Availability: Please contact the authors. JEL Classifications: M41; G11; G4; C91; D83.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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