A dynamic context management infrastructure for supporting user-driven web integration in the personal web
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
Most web applications deliver personalized features by making decisions on behalf of the user. Thus, the user's web experience is still a fractionated process due to a lack of user-centric web integration. In contrast, smarter web applications will empower the user to control the integration of web resources according to personal concerns. Moreover, as the user's situation and web resources continuously evolve, web infrastructures supporting smarter applications require dynamic and efficient mechanisms to represent, gather, provide, and reason about context information. Aiming at optimizing the user's web experience, this paper proposes a self-adaptive context management infrastructure, and an extensible context taxonomy based on the resource description framework (RDF). Our context manager is able to deploy new context management components to keep track of changes in the user's situation at run-time. Our taxonomy includes a set of inference rules for supporting dynamic context representation and reasoning. Using a smarter commerce case study, we illustrate the application of feedback loops and semantic web, to the realization of dynamic context management in the personal web.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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