The Fight Against Corruption and Money Laundering: The Office of the Asset Recovery and Management
Bibliographic record
Abstract
The art.367 septies, was added to the Criminal Procedure Law (LECrim in Spanish) through Final Disposition 1º number three of the Organic Law 5/2010, of 22 June, which modified the Organic Law 10/1995, of 23 November of the Criminal Code. Thus, for the first time, the creation of an Office dedicated to Asset Recovery (OAR). Although considered necessary, this legislative amendment was never implemented in practice and its functions were assumed in part by the Center for Intelligence against Terrorism and Organized Crime (CITOC) and the National Plan on Drugs (Act 17/2003 of 29 May). Furthermore, since it was designed solely to carry out asset tracing tasks and not to manage them, the judicial bodies lacked the necessary assistance to be able to administer and perform the effects seized and confiscated. As a result, they were faced with the task of managing assets that had previously been seized and confiscated for which they lacked the necessary material and human resources. However, the need to comply with Directive 2014/42/EU of the Parliament and Council of 3 April 2014 on the freezing and confiscation of instrumentalities and the proceeds of crime in the European Union and, as a result, to improve the effectiveness of the system of asset recovery and management, led to the amendment of Organic Law 1/2015 of 30 March, which amended Organic Law 10/1995, by means of the fifth Additional Provision of the LECrim (introduced by Law 41/2015, of 5 October, amending the Criminal Procedure Act to speed up criminal justice and strengthen procedural guarantees) the content of art.367 septies of the LECrim, in the sense of completing its initial function of locating and recovering assets with that of administering and managing them, made clear by renaming the initial OAR (Office of Asset Recovery) into the current OARM: Office of Asset Recovery and Management.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".