Bibliographic record
Abstract
Abstract Identity theft commonly refers to the illegal theft and misuse of another person’s identity information, resulting in a benefit to the offender or harm to the victim. With the rise of technological payment systems, identity theft increased dramatically in the 1990s and 2000s and impacts almost 1 in 10 adults annually. Identity theft can be difficult to measure, in part because few victims report it to law enforcement and government agencies and because victims often have limited knowledge about how their information was obtained and misused. Identity theft can involve the misuse of existing bank, credit, or other accounts, the creation of new accounts, or other fraudulent misuses of personal information. Moreover, the methods of acquiring identity information vary and include hacking, phishing, and stealing physical documents. While identity theft’s rise results from increasing technological reliance, the relative prevalence of online and offline forms remains unknown. The limited research on identity theft offenders finds that their motives and techniques vary, but that committing identity theft is usually a rational choice and that offenders often use techniques to neutralize identity theft behaviors. More research exists on identity theft victims, due, in part, to identity theft victimization surveys, which find that victims face a range of consequences and reporting options. Globally, both criminal and consumer protection laws have been implemented or modified to respond to identity theft, although victims must typically advocate for themselves to resolve identity theft’s consequences.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".