How do Different Sources of the Variance of Consumer Ratings Matter
Why this work is in the frame
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Bibliographic record
Abstract
To examine the effect of the variance of consumer ratings on product pricing and sales we develop a model which considers goods that are characterized by two types of attributes: experience attributes and experience attributes that were transformed in search attributes by consumer ratings that we call informed search attributes. For pure informed search goods, we find that with increasing variance optimal price increases and demand decreases. For pure experience goods, we find that with increasing variance optimal price and demand decrease. For hybrid goods, when there is low total variance and the average rating and total variance are held constant, optimal price and demand increase with increasing relative share of variance caused by informed search attributes. Via this mechanism, risk averse consumers may prefer higher priced goods with a higher variance. In addition, our model provides a theoretical explanation for the empirically observed j-shaped distribution of consumer ratings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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