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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".