All External Reference Prices Are Not the Same: How Magnitude, Source, and Fairness Shape Payment for Digital Goods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Music, movies, e-books, news: all industries that have been impacted by free distribution of their products. For many individuals, this wide availability of free substitutes drives users’ willingness-to-pay down. In this environment, how can platforms motivate consumers to pay for goods that they may be able to get for free? We demonstrate providing flexibility in payment through allowing users to “pay what you want,” along with providing external reference prices (ERPs) set by different sources, that is, other similar consumers or the platform itself, can influence payment. Importantly, a site-set ERP has more influence increasing payment than a socially-set ERP. An interesting nuance to this is that when the ERP is perceived to be high, the marginal effect of an increase in ERP on payment is smaller than when it is perceived to be fair; in other words, providing a fair ERP is more effective in increasing payment than providing an ERP that is too high. Altogether, platforms can leverage these findings in designing interfaces to provide information that can motivate consumers to pay for digital goods.
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.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| 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 it