Estimating the destination of Mexican-based laundered funds: an application of the modified Walker-Unger model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This study aims to apply the modified Walker-Unger model to show the degree of attractiveness of a country for Mexican-based money launderers to send their illicit funds for the 2000–2015 time period. Design/methodology/approach The modified Walker-Unger model is used to conduct the analysis, as it combines several independent variables related to an illicit financial activity. These allow the researcher to investigate the attractiveness of a market to money launderers and the possible economic effects of money laundering. In total, 13 categories of indicators were used, namely, gross national product per capita; banking secrecy; government attitude; society for worldwide interbank financial telecommunication membership; financial deposits; conflict; corruption; Egmont group membership; language; trade; culture, colonial background; and physical distance. Findings Model results suggest the preferred destinations for Mexican-based money launderers from 2000 to 2015 were Bermuda (i.e. from 2000–2004), Canada (i.e. in 2005 and 2006) and Monaco (i.e. from 2007–2015). Research limitations/implications Timing and availability of reliable data after 2015. Practical implications Aids in continuing to empirically validate the Walker-Unger model. There is little literature on models that quantify money laundering activity. Social implications May aid policymakers in targeting anti-money laundering policy to more relevant countries. Originality/value The first empirical investigation that looks to quantify money launderer activity in Mexico. Contributes to the limited literature of quantitative investigations on money laundering.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it