The Effect of Advice Valence on the Perceived Credibility of Data Analytics
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT We use an experiment to examine how advice valence (i.e., whether the advice suggests good news or bad news) affects the perceived source credibility of data analytics compared to human experts as a result of motivated reasoning. We predict that individuals will perceive data analytics as less credible than human experts, but only when the advice suggests bad news. Using a forecasting task in which individuals are seeking advice from either a human expert or data analytics, we find evidence consistent with our prediction. Furthermore, we find that this effect is mediated by the perceived competence of the advice source. We contribute to the nascent accounting literature on data analytics by providing evidence on a potential impediment to successfully transitioning to the use of analytics for decision-making in organizations.
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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 | 0.022 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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