Familiarity and Trust: Measuring Familiarity with a Web Site.
Bibliographic record
Abstract
Abstract — This work aims at measuring familiarity to contribute to the formalization of trust. Trust has always been bundled with familiarity to become a popular topic in the areas of psychology, sociology and computer science. Correlation between familiarity and trust has been explored and proved by many studies from different perspectives. A new model of trust has been proposed by Carter and Ghorbani to formalize the value-centric trust in agent societies. However, the measurement of familiarity in their work is roughly the similarity of values between two agents. Familiarity measurements proposed by other researchers are not convenient due to the instability and abstruseness of familiarity, or are useful only in certain circumstances and are quite problem-specific. We propose a convenient way of measuring familiarity with a Web site and continuously updating its value based on the exploration of factors that may affect familiarity. The five major factors include prior experience, repeated exposure, study duration, level of processing and forgetting rate. The human factors are mapped to the properties of Web application domain through a factors hierarchy. Experiments to evaluate the performance of the proposed measurement are discussed in the future work section. I.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".