Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it