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
The return of a product is often one of a series of transactions that a consumer undertakes in search of a good. In this article, the authors analyze returns as part of a product search process: Upon returning a product, consumers may immediately purchase an alternative one, which they may later replace with another product, and so on, until they either ultimately keep their last purchase (Keep outcome) or not (No-keep outcome). Such a sequence of transactions is called a “product return episode”. In this work, the authors study consumer Keep and return abuse behavior using episodic metrics. Using data from a consumer electronics retailer, the authors show that analysis of product returns with episodic metrics provides insights that differ from, and go beyond, analyses with commonly-used transactional metrics. They find that although higher average price and larger store assortment at a subcategory level both tend to increase the return probability, they also increase the probability of keeping a product at the end of an episode, which points to profit-improving opportunities for retailers by allowing returns and tracking episodes. They also find that episodic metrics are useful for identifying return abuse.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it