A Visualization Approach for Modeling Trust in E-Commerce
Bibliographic record
Abstract
E-commerce is presently operating under its expected capacity, mainly because traders find it very difficult to trust one another online for trading decisions. It is therefore very important to develop an effective trust management system that assists e-commerce participants to make good trust decisions. This paper describes a visualization approach towards such a system. The benefits of this approach are threefold: first, it gives a better understanding of the components that can be used in a trust management system. Secondly, it illustrates that the components contributing to the trust making process can be different from one environment to another. Thirdly, it shows that the way one person trusts can be different from others. This approach, rather than using the same static attributes to calculate trust for everyone, uses specific attributes based on each truster's goals. Moreover, by using GRL and UCM as notations for trust modeling, this approach provides a visual representation of trust, its components and the trusting process.
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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 | 0.001 | 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".