Familiarity and Trust: Measuring Familiarity with a Web Site.
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
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.
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) | 0.000 | 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