Rating Anticipation and Strategic Downward Selection in Consumer‐Generated Rating Systems: Evidence from a Peer‐to‐Peer Platform Market
Bibliographic record
Abstract
Abstract This paper examines how sellers use strategic downward selection to game consumer‐generated rating systems. I highlight the role of rating anticipation in sellers' selection of buyers – concerns regarding buyers' post‐hoc evaluations of a seller influence whom she chooses to transact with at the outset. To reduce evaluation anxiety, sellers strategically avoid buyers with superior market standings, preferring those with an inferior standing, who are perceived as more likely to be satisfied and provide positive evaluations. Analysing nearly half a million transactions on a major peer‐to‐peer lodging platform in which all participants list their homes, I find that hosts are more inclined to approve requests from guests with inferior homes. This tendency is stronger when hosts experience rating declines, heightening their evaluation anxiety. It is also more pronounced among experienced hosts who better understand consumer‐generated rating systems, and when hosts and guests are in the same country, facilitating social comparisons.
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How this classification was reachedexpand
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 | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".