Bibliographic record
Abstract
This paper formalizes the semantics of trust and studies the transitivity of trust. On the Web, people and software agents have to interact with "strangers". This makes trust a crucial factor on the Web. Basically trust is established in interaction between two entities and any one entity only has a finite number of direct trust relationships. However, activities on the Web require entities to interact with other unfamiliar or unknown entities. As a promising remedy to this problem, social networks-based trust, in which A trusts B, B trusts C, so A indirectly trusts C, is receiving considerable attention. A necessary condition for trust propagation in social networks is that trust needs to be transitive. However, is trust transitive? What types of trust are transitive and why? There are no theories and models found so far to answer these questions in a formal manner. Most models either directly assume trust transitive or do not give a formal discussion of why trust is transitive. To fill this gap, this paper constructs a logical theory of trust in the form of ontology that gives formal and explicit specification for the semantics of trust. Based on this formal semantics, two types of trust -- trust in belief and trust in performance are identified, the transitivity of trust in belief is formally proved, and the conditions for trust propagation are derived. These results give theoretical evidence to support making trust judgment using social networks on the Web.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".