A dynamic context management infrastructure for supporting user-driven web integration in the personal web
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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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".