The Principles of Distinction and Proportionality Under the Framework of International Criminal Responsibility - Content and Issues
Bibliographic record
Abstract
Este artículo busca ilustrar cómo los Principios de Distinción y Proporcionalidad, provenientes de un conglomerado de normas primarias (Derecho Internacional Humanitario, DIH), han influenciado el sistema de responsabilidad penal internacional, consagrado en el Estatuto de Roma. Se observa que aun cuando este último contiene provisiones legales que reprochan conductas de tipo indiscriminado, hay un vacío relacionado con el significado y la extensión de dichos comportamientos; dicha problemática se explica, de un lado, por la reticencia que tienen los Estados en comprometer su soberanía y, del otro, por las especificidades de la función punitiva de la Corte. Las consecuencias prácticas de esta situación se pueden apreciar en el escenario de los conflictos armados internos, ya que la mayoría de éstos se desarrolla en este ámbito. A pesar de un diagnóstico pesimista, debe señalarse que el mero hecho de que una corte penal permanente haya emergido como una realidad tangible constituye unaganancia, ya que es mediante su actividad –la producción de jurisprudencia que establezca el contenido y alcance de las normas– que los inconvenientes pueden ser confrontados y resueltos.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.079 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".