Decisions, Decisions: An Analysis of Identity Theft Victims’ Reporting to Police, Financial Institutions, and Credit Bureaus
Bibliographic record
Abstract
Identity theft, the theft and misuse of another person’s identifying information, impacts approximately one-in-ten American adults annually. Despite its prevalence, low police reporting rates by victims means that the dark figure of identity theft remains substantial, with official statistics representing few cases. Instead, many identity theft victims report to credit card companies and banks, and some report to credit bureaus or other private institutions. Drawing on the 2016 National Crime Victimization Survey – Identity Theft Supplement, this paper investigates identity theft victims’ decisions to report to law enforcement, financial institutions, and credit bureaus. It finds that along with situational factors, measures of seriousness impact reporting to these institutions and most strongly predict reporting to law enforcement. Moreover, this paper tests for interaction effects between paying out of pocket for losses and the other measures of seriousness and finds that victims who pay out of pocket have distinct reporting patterns compared to those who are reimbursed. This paper thus contributes by improving our understanding of the nature of victimizations that come to the attention of identity theft’s various responding institutions.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".