Managing Dynamic Context to Enable User-Driven Web Integration in the Personal Web
Bibliographic record
Abstract
The Personal Web is the people-centric instan-tiation of the Smart Internet where informa-tion systems, services and web content are ar-ticulated by users according to their matters of concern. To realize the vision of the Per-sonal Web, the Smart Internet requires infras-tructure to support the user in the integra-tion of personal data and the composition of personal services within a highly dynamic con-text that constitutes the user's Personal Web Sphere. To address these requirements, we pro-pose a user-driven context management frame-work, built on the top of the basic enabling infrastructure of the Personal Web, to support users in the run-time modification of personal context models. The core of our proposal is the management of monitoring concerns by imple-menting feedback loops, where the user acts as the planner of the controller to adapt the mon-itoring strategy by means of using web inter-actions to modify the personal context models. These context models, deployed at three differ-ent levels of abstraction, represent monitoring concerns by defining abstract types of contex-tual entities, the relationships among them and the interactions that the user can instantiate to drive web integration.
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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 | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".