Semantic web trust : the next step in web evolution
Bibliographic record
Abstract
The inception of the World Wide Web marked the beginning of a new age, an age where information is easily distributed and accessible to everyone. Years after it was conceived, the net is still growing at a higher rate than ever. It is becoming ever more apparent that the World Wide Web’s current software infrastructure will need to evolve if it is to remain a reliable and dependable resource. In this paper we will be looking into the facets that make web content accessible and reliable. We will also be proposing a structure that makes use of technologies such as the Semantic Web and Agent Technology to help resolve data access and classification issues. The approach that we are proposing will involve the creation and distribution of data tags, policies and reasoners. We will also show how these items can be used by entities such as software agents or users to specify access control, classify the resources in terms of relevance and to decide on how trustworthy the information being used is.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.009 | 0.038 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".