Bibliographic record
Abstract
E-commerce supply channels for food that focus on “B-2-C” (business to consumer) marketing face a number of challenges. E-commerce channels, however, may also allow firms marketing specialty livestock products such as bison, wild boar and ostrich a unique opportunity to access widely dispersed and distant niche markets. A number of factors that appear to be important for the success of e-commerce marketing of food products have been identified—offering a variety of food products, online payment systems, offline payment systems, delivery methods, selling to customers in other countries, quality control during shipment and customer feedback. The objective of this research is to obtain information on two aspects of each of these attributes for firms engaged in B-2-C marketing of food products. The two aspects are: (1) the firm's assessment of the attribute's importance for success of e-commerce marketing and (2) the extent to which firms were satisfied with that aspect of their e-commerce marketing. Results suggest that, with the exception of the ability to access international markets, these aspects of e-commerce marketing should not represent an important constraint to the success of B-2-C marketing of specialized livestock 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.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.103 | 0.024 |
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".