Systems Plan for Combating Identity Theft – A Theoretical Framework
Bibliographic record
Abstract
The Internet has made it easier for individuals and organizations to communicate and conduct business online. At the same time, personal, commercial, and government information has become a target for identity theft. The incidences of identity theft have increased substantially in the Internet age. Increasing news reports of bank/credit cards theft, as-sumed identity for economical and criminal activities has created a growing concern for individuals, businesses, and governments. As a result, it’s become an important and urgent task for us to find managerial and technical solutions to combat identity fraud and theft. Solutions to identity theft problem must deal with multiple parties and coordinated ef-forts must be made among concerned parties. This paper is to provide a comprehensive view of identity theft issue from system planner’s perspective. The roles of identity owner, issuer, checker, and protector, are examined to provide a starting point for organizational and information systems design.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".