What Is ‘European’ about White-Collar Crime in Europe? Perspectives from the Global South
Bibliographic record
Abstract
Introduction It is truly a pleasure for me to be able to provide my point of view to such a relevant work that brings together such prestigious authors and interesting lines of inquiry. My contribution is based on certain impressions of European works on white-collar and corporate crime that I have read previously and, in a more analytical way, of the contributions to this volume. To offer a complementary view from South and Central America, such a vast territory, it is necessary to briefly introduce myself. I am a former magistrate and current trial lawyer in economic crime courts in Buenos Aires, Argentina, where I graduated and where I am currently a professor. I previously lived and studied in Barcelona, where I got my PhD specializing in the criminology and sociology of punishment. Academic activity has continued to connect me with Europe and the United States, and more recently with Australia. This has led me to become very interested in the similarities and differences in the explorations of the criminal justice system and of criminological theory and to participate in the reflection on a criminology of the Global South, or ‘southern criminology’. Most of the countries of South and Central America constitute Latin America (along with Mexico in North America). The Spanish and Portuguese colonization not only defined an important historical heritage, with national and cultural varieties and two Romance languages, but also a criminal legal tradition of civil law and an inquisitive system typical of continental Europe, which only in the last 30 years has been shaken by important reforms towards the adversarial system of English-speaking countries, although particularly toward that of the US (Langer, 2007). Compared to the US and Canada, South and Central America has a lot of historical and cultural diversity, beyond a common past provided by colonization. Adding to this, there are series of subaltern indigenous theories, practices and institutions that have wide differences according to the territory, and which are beginning to inspire the literature of ‘green criminology’. On the other hand, for those countries that participate in what is usually called the Global South, both North America and Europe are part of the Global North axis and we necessarily treat Anglo–American countries as part of the same legal and criminological community.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".