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
Purpose This study associated consumers' food choice motives and socio-demographic characteristics with their attitudes and consumptions towards food shopping with four e-commerce modes: business-to-consumer (B2C), online-to-offline delivery (O2O Delivery), online-to-offline in-store (O2O In-store) and New Retail. It also explored consumer preferences for specific food categories within the four e-commerce modes. Design/methodology/approach An online survey was administered to 954 participants from three Chinese cities: Beijing, Shanghai and Shenzhen. Descriptive analysis and linear regression were used in the data analysis. Findings The following food choice motives (FCMs) and socio-demographic characteristics had a significant effect on food e-commerce attitudes and/or consumption, with some or all of the four e-commerce modes: Taste Appeal, Value for Money, Safety Concerns, Quality Concerns, Processed Convenience, Purchase Convenience, Others' Reviews, City, Gender, Household Size, Age, Income, Occupation and Marital Status. Consumers also have different consumption preferences for food categories in the four e-commerce modes. Originality/value This is the first study to associate consumer FCMs and socio-demographics with their e-commerce attitudes and consumption regarding food in four e-commerce modes: B2C, O2O Delivery, O2O In-store and New Retail.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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