The State of the Art in Trust and Reputation Systems: A Framework for Comparison
Bibliographic record
Abstract
We introduce a multidimensional framework for classifying and comparing trust and reputation (T&R) systems. The framework dimensions encompass both hard and soft features of such systems including different witness location approaches, various reputation calculation engines, variety of information sources and rating systems which are categorised as hard features, and also basic reputation measurement parameters, context diversity checking, reliability and honesty assessment and adaptability which are referred to as soft features. Specifically, the framework dimensions answer questions related to major characteristics of T&R systems including those parameters from the real world that should be imitated in a virtual environment. The proposed framework can serve as a basis to understand the current state of the art in the area of computational trust and reputation and also help in designing suitable control mechanisms for online communities. In addition, we have provided a critical analysis of some of the existing techniques in the literature compared within the context of the proposed framework dimensions.
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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 | 0.025 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.023 | 0.038 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".