La garantía de la defensa eficaz en el marco del Código Procesal Penal Peruano
Bibliographic record
Abstract
La investigación tiene como problema general la siguiente interrogante\n¿Cómo influye la Garantía de Defensa Eficaz en el decurso ordinario del Proceso\nPenal Peruano?, cuyo objetivo es “Analizar cómo influye la Garantía de Defensa\nEficaz en el decurso ordinario del Proceso Penal Peruano”, y como supuesto “La\nGarantía de la Defensa Eficaz influye significativamente en el decurso ordinario\ndel Proceso Penal Peruano”; habiéndose utilizado como metodología general:\ninductivo – deductivo y el analítico – sintético.\n\nLa conclusión a la que se arribó “Se determinó que la Garantía de la\nDefensa Eficaz, si influye en el decurso ordinario del Proceso Penal, ya que\ncontiene sub-garantías que asumen un papel fundamental e imprescindible para\nla validez y legitimación del proceso penal; por ende, su ausencia deviene en la\nnulidad de este, y con ello corregir las deficiencias de la protección de principios\nprocesales mínimos”.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".