SmartData: Make the data “think” for itself
Bibliographic record
Abstract
SmartData is a research program to develop web-based intelligent agents that will perform two tasks: securely store an individual’s personal and/or proprietary data, and protect the privacy and security of the data by only disclosing it in accordance with instructions authorized by the data subject. The vision consists of a web-based SmartData agent that would serve as an individual’s proxy in cyberspace to protect their personal or proprietary data. The SmartData agent (which ‘houses’ the data and its permitted uses) would be transmitted to, or stored in a database, not the personal data itself. In effect, there would be no personal or proprietary “raw” data out in the open—it would instead be housed within a SmartData agent, much like we humans carry information in our “heads;” extending the analogy, it would be the “human-like clone” that would be transmitted or stored, not the raw data. The binary string representative of a SmartData agent would be located in local or central databases. Organizations requiring access to any of the data resident within the agent would query it once it had been “activated.” In this paper, we provide a preliminary overview of the SmartData concept, and describe the associated research and development that must be conducted in order to actualize this vision.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.005 | 0.001 |
| 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 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".