Goodreads Versus Amazon: The Effect of Decoupling Book Reviewing And Book Selling
Bibliographic record
Abstract
Book reviewing is a commonplace activity on many ecommerce sites. However, it is nested within the broader context of book buying and selling. Goodreads, an online platform for social curation of book collections, provides an opportunity to observe on-line book reviewing in an environment that is not (at least overtly) focused on commercialization. In this study, we perform a careful comparative study of reviewer behavior and engagement in Goodreads and Amazon.com, constrained to a single genre (biography), including 21,394 books and 2.5 million reviews. We discover marked differences between the platforms that suggest disparate population composition and objectives of review-writing across the two platforms. Our findings suggest an important and generalizable principle: that two platforms engaging users on the same task (e.g., book review writing) may elicit quite different behavior depending on the implicit or explicit context and motivation present.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".