Bibliographic record
Abstract
There has been intensive research on user-controlled privacy from the perspective of agent automation of privacy related user tasks. The W3C's Platform for Privacy Preferences (P3P) specifies standards that can be used by P3P agents to automatically retrieve a web-site's privacy policy on how the users' data is collected, stored, and managed, and then to determine whether they are compatible with the user's privacy preferences. Current approaches to managing user's privacy do not capture context, are not user-friendly, and do cater well to the dynamic nature of privacy preferences very well. Clearly, the user's privacy preferences depend on the context of the user's online activity and the user's preferences evolve with user's experience and changing levels of trust in various organizations and domains. We propose a model for user's privacy preferences that incorporates the context for user activity and we apply it using a Case-based Reasoning (CBR) approach that relates the current activity to previous activities stored in the case-base and thus forms an intuitive and understandable process. We describe the context model for privacy preferences, how CBR is used to create a new contextual case from the web-site's privacy policy and the user's current activity, and how the CBR retrieves matching cases to be applied to the retrieved privacy policy.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".