Eficacia sociojurídica y ambiental del delito “Actos en perjuicio de la biodiversidad”, del Artículo 254.1. del Código Penal Cubano
Bibliographic record
Abstract
For 35 years, the same ones that the recently repealed substantive criminal law of Cuba was in force, a considerable part of civil society, academics and, above all, national and foreign environmentalists yearned for its inclusion in a new legal text, or through the modification of the existing one, of those behaviors that are socially harmful to the environment to which a mere and ineffective administrative treatment was given by way of contravention repression. The wait was answered with the issuance on September 1, 2022 of Law 151, "Criminal Code", in which an entire chapter was incorporated in this regard; however, with respect to one of the most important criminal types, the one provided for in article 254.1. and identified as “Acts detrimental to biodiversity”, there are irregularities that lead the authors to consider that, since its entry into force in the last quarter of 2022 and during the development of the first semester of 2023, it is socio-legal and environmentally ineffective, taking into account, firstly, the non-existence of legal norms that complement it and, secondly, its structural elements. For this reason, it is intended to assess the socio-legal and environmental effectiveness of the inclusion of this crime, taking as reference ten theses submitted to the criteria of 30 experts rigorously processed with the use of the Delphi method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".