Bibliographic record
Abstract
How reliable are online ratings? This thesis examines whether and why online ratings are inconsistent across websites.The first essay (Chapter 2) identifies a key predictor of consumer reviews: perceived website main functions. Specifically, I distinguish between transaction sites (websites primarily selling products, e.g., Amazon) and communication sites (websites primarily facilitating information exchange, e.g., Goodreads), finding that merely changing the perceived main function of a website can alter consumer ratings for the exact same product. I first validate the transaction-communication distinction in describing real-world platforms that offer consumer reviews with a large-scale pretest. Then, across five studies, including secondary data analysis, lab, and online experiments, I document the existence of an evaluation inflation phenomenon: for the same product, the average rating and proportion of five stars are higher on transaction sites compared to communication sites or a control group (making no mention of platform). This effect persists among various product categories, real-world platforms, manipulations of website main functions, and whether the evaluated products were provided by the experimenter or actually purchased by the consumers. The second essay (Chapter 3) proposed and tested a multi-driver social influence (compliance) account explaining the evaluation inflation phenomenon. Across two pretests and six studies, I show that consumers perceive a higher expectation to give five stars on transaction sites, and comply with this expectation by boosting their ratings. This perceived expectation comes from two major sources: the expectation of sellers and the perceived consensus of other consumers. Following this theory, I also develop and prove three simple intervention strategies that effectively mitigate evaluation inflation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.440 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".