Reputation-Based Trust Assessment of Transacting Service Components
Bibliographic record
Abstract
We witness a paradigm shift where entities such as software applications, people, businesses and service providers increasingly interact in virtual rather than physical environments, such as social media, social commerce, and the metaverses. A key issue that emerges in such environments is how one entity can trust another. Here, the concept of trust is considered as a meta-requirement, that is, the level of belief a service requestor has that a service provider will provide the service in a way that meets the requestor’s expectations. We refer to the service offering entities as service providers (SPs) and the service requesting entities as service clients (SCs). In this paper, we propose a technique that allows for evaluating trust and assigning reputation to various service providers by considering first their ability to fulfill their clients’ expectations or policies, and second the reputation other service clients have, when acting as recommenders for the aforementioned service providers. In this work, service clients and service providers are considered as virtual entities that coordinate with each other, and may include physical users, avatars, micro-services, software agents, smart contracts or any other distributed inter-networked resource, without making any assumptions as to what a service client or a service provider entity is as long as it participates in an interaction.
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".