Bibliographic record
Abstract
Este estudio se centra en el movimiento de flujos financieros ilícitos, específicamente efectivo, hacia el sistemafinanciero legal, en formas que buscan ocultar los orígenes del efectivo, haciendo que el mismo sea imposible de rastrear.A través del análisis de varios casos judiciales, esta investigación muestra cómo, dentro de las normas contables, los flujosfinancieros ilícitos se estructuran y se trasladan a las instituciones financieras, de manera que pasan desapercibidos. Eldocumento también destaca las deficiencias de control interno en los bancos que propagan estas prácticas negativas.El presente trabajo se basa en investigaciones anteriores sobre fraude y corrupción (Compin, 2008; Cooper et al., 2013;Everett et al., 2007; Mitchell et al., 1998; Neu et al., 2013b) y agrega ejemplos mediante el uso de casos judiciales reales pararesaltar las prácticas corruptas. Se consideran además los métodos para prevenir estas prácticas negativas y se detallacómo se adaptan las prácticas y técnicas contables para ayudar con las estrategias de lavado de dinero. En muchos casos,estas prácticas se propagan y mantienen durante periodos prolongados, ya que no son detectadas por los mecanismos decontrol establecidos para su prevención e identificación.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".