Trust in the Social Web Applications in Recommender Systems and
Bibliographic record
Abstract
The portion of daily business and socialising conducted on the Web is in-creasing at a rapid rate. In February 2006, online auction giant eBay inc. reported annual growth rate to be in the region of 29 % [27] with net revenues of $1.7 billion for the last quarter of 2006 alone. eBay has over 200 million registered users and receives over a billion page views per day [27]. In 2006, the online store and recommender system Amazon.com was ranked 272 on the Fortune 500, with a revenue of 8,490 million dollars [1]. From July 2006 to July 2007 the social networking site Facebook grew from 7.5 million users to 30 million. [25] Despite these huge figures, the technology behind Web applications is still very much in its infancy. Every day, people are forced to make important trust decisions about strangers on the Web with only a limited amount of information by which to assess and evaluate the person they are transacting with. The rapid growth of ”Social ” Web applications, in which people can
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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) | 0.000 | 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 | 0.000 |
| 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".