Product Customization via Starting Solutions
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
Customizing a product by choosing each of its attributes individually tends to be onerous for consumers, and the benefits of product customization may thus be offset by an increase in choice complexity. As a remedy for this dilemma, the current research introduces the customization via starting solutions (CvSS) architecture, which substantially reduces the complexity of product customization while preserving all of its advantages. Under CvSS, consumers first select one starting solution from a set of prespecified products, which they then refine to create their final customized product. Evidence from nine studies (three of which were conducted in field settings) across a wide range of product domains (shirts, cars, vacation packages, jewelry, and financial products) shows that the CvSS architecture results in substantial benefits relative to the standard attribute-by-attribute product customization format for both consumers (increased satisfaction with their product choices, reduced choice complexity, and enhanced mental simulation of product use) and firms (purchases of more feature-rich, and thus higher-priced, products).
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 | 0.047 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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