Identification in electronic networks
Bibliographic record
Abstract
Identification in electronic networks (e-Identification) has been a source of new risks in e-commerce and e-government [3, 22]. Identity theft is the fastest growing white-collar crime in many countries, specifically in developed countries. The identity theft (IDT) report presented by Competition Bureau Canada indicates that the cost of IDT to the economy of Canada has increased by 83% in the period of 2002-2003. Also, the average loss of victims has increased from 1439Cnd$ in 2002, to 1614Cnd$ in 2003. This increase can also be observed in other developed countries at the same level. This rapid increase of identity theft indicates that the development of optimal identification systems in organizations is far behind the development of electronic networks. One of the central issues in IDT is the identification process. Understanding the identification processes in electronic systems, its concepts, and tools are the fundamentals of designing, implementing and operating an efficient and effective identification system. However, there have been very few studies in this area, and there is a gap in literature specifically on identification tools, their characteristics, their efficiency and their effectiveness, and their usability. This paper presents a comprehensive model representing virtually all of the commonly used identifiers in electronic networks and their characteristics, and also presents measures of efficiency and effectiveness of identification tools in electronic networks.1
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".