FRAGILIDADES DO COMBATE À LAVAGEM DE DINHEIRO NAS REGIÕES DE FRONTEIRA
Bibliographic record
Abstract
Lavagem de dinheiro, evasão de divisas, descaminho, regiões de fronteira são palavras que se podem associar, facilmente, ao artigo a seguir. Busca-se entender as possíveis fragilidades no combate à lavagem de dinheiro, em especial do ponto de vista de instituições financeira de fronteiras. Analisa-se a importância do colaborador e um paralelo com as novas operadoras de mercado financeiro, as fintechs. Utiliza-se uma metodologia de pesquisa bibliográfica, análises de cenários e ferramentas de combate, além da atualização com a nova circular do Banco Central (BACEN), em que são explícitos direcionamentos para agências de localização de risco, fronteiras. Ao final, concluímos com a necessidade de uma presença nas regiões de fronteiras, de forma intensa, com tecnologia, com atuação pontual de colaboradores.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".