Inherent Issues with User-Generated Star Ratings
Bibliographic record
Abstract
User-generated ratings—“star ratings”—are among the most influential sources of information for consumers online. In this dissertation, I provide two arguments to suggest ratings may be a flawed source of information for consumers. Both demonstrate that ratings for products are highly context-dependent. Chapter 2 focuses on mental context, showing that ratings are created with respect to a frame of reference a specific product engenders, including expectations for quality/experience. Because different products can elicit different frames of reference, ratings are not comparable across alternatives. Chapter 3 then shows the similar influence of consumption context—raters’ physical context at the time of consumption—on ratings. Ratings are said to benefit consumers by conveying the value consumers receive by consuming a product. However, consumption experience is a function of both features intrinsic to the product and incidental aspects of consumption context. I find that consumption context can bias individual ratings, which is problematic if prospective consumers use ratings as a proxy for a product’s intrinsic quality. In both essays, my findings have implications for consumers and platforms. For consumers, I show that differences in ratings are often uninformative of product quality, making ratings a poor proxy for one’s future experience. For platforms, my results demonstrate the need to improve how ratings are created and later shown to consumers. All code, data, materials, and pre-registration documents can be found on OSF at: https://osf.io/5yr34/?view_only=20bbff1825c040ae8c4e4052475e0988
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.236 | 0.600 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".