Bibliographic record
Abstract
Since the commercialization of the Internet in the 1990s, online retailing has increased steadily. According to the most recent Department of Commerce Census Bureau report,1 retail e-commerce sales in the first quarter of 2004 were $15.5 billion, up 28.1% from the first quarter of 2003. E-commerce sales in the first quarter of 2004 accounted for 1.9% of total sales, compared with 1.6% of total sales for the first quarter of 2003. An important trend in this growth in B2C (business-to-consumer) e-commerce is the participation of small business on the Web. Considering that in the United States small business comprises more than 99% of employer firms,2 this trend is significant. Though the Web offers huge potential to these small businesses for growth and prosperity, and also offers them a very low entry cost, Web visibility becomes the major barrier for them. Small businesses often have difficulty putting up enough funding to compete with brand-name businesses in promotion. So small businesses are desperately in need of a less costly channel for increasing their Web visibility. In the past 4 years and especially since the economic slowdown in 2000, comparison shopping has become more and more popular among online shoppers. Because of the low cost of being listed on comparison-shopping Web sites and the relatively high conversion rate for online shoppers who use comparison shopping, many small businesses found this an ideal channel to increase their Web visibility. As a result, many early participating small businesses gained a customer base in the competition by displaying their products and service prices on comparison-shopping Web sites. Now, it is more and more clear that comparison shopping provides a unique opportunity for small businesses to reach a large customer population with relatively little cost. To help readers better understand this phenomenon, we give a comprehensive introduction to comparison-shopping agents and summarize recent research on their impact in e-commerce.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.002 |
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".