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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.029 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.015 |
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".