Differences in “Truthiness” across Online Reputation Mechanisms
Bibliographic record
Abstract
Using a unique dataset from a specialty online retailer, we compare the reputation mechanisms of Amazon and eBay. We develop a theoretical model of reputation mechanisms based on the feedback processes at Amazon and eBay, and then validate this model by comparing its predictions to the findings of existing research. We construct and estimate a dynamic simultaneous equations model of the sales demand, feedback generation, and price formation processes. Significant differences exist across the Amazon and eBay mechanisms in that: (a) the propensity to leave feedback is higher on eBay than on Amazon, (b) negative feedback is much less frequently observed on eBay than on Amazon, (c) feedback on Amazon has a strong demand-inducing impact, raising prices and total sales, whereas (d) feedback on eBay has no effect on demand. Overall, these results suggest that the Amazon reputation mechanism provides more useful information to buyers than does the eBay mechanism, and that these differences are largely due to the directionality of these mechanisms: eBay’s bilateral system, which allows buyers to rate sellers and sellers to rate buyers gives way to strategic considerations – reciprocity and retaliation – that do not plague Amazon’s unilateral system in which only buyers rate sellers.
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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".