Privacy And Web 3.0: Implementing Trust and Learning from Social Networks
Bibliographic record
Abstract
After having shifted from Web 1.0 to Web 2.0, scientists welcome the advent of Web 3.0, an environment where meaning is added to data. While in the Semantic Web people are no longer users, but part of the emerging applications, producers, subjects and beneficiaries of the Big Data, however, opaque processing of personal data poses tremendous risks and dangers for individuals. Given the new era of Big Data this paper studies firms’ purposes and practices to detect some emerging privacy risks. Moreover, theories that deal with social networks are examined to conclude that, even if people state that they value their privacy, however, they often disclose a huge volume of personal information. Taking into account that today’s European concept of privacy is conceptualized in negative terms this paper also proposes the implementation of trust and loyalty into the privacy concept through flexible fiduciary laws. Furthermore, data portability is discussed to detect its potential as a strategic feature, a key tool that will enhance trust. Finally, further scenarios and proposals are submitted, in our attempt to answer the question whether the European concept of privacy could be re-shaped for the benefit of individuals.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".