Editorial of dossier “Admissibility of Evidence in Criminal Process. Between the Establishment of the Truth, Human Rights and the Efficiency of Proceedings”
Bibliographic record
Abstract
The rules on the admissibility of evidence secure accurate fact-finding as a prerequisite for the correct application of substantive criminal law and proper operation of the criminal justice system in society. But the search for the truth must be limited in order to take into account other important values, among which human rights hold a central place. The quest for a fair balance between the effective fight against crime and respect for individual rights constantly remains in the center of heated discussion. However, there are two other factors that strongly influence the evidentiary rules, creating an environment where finding the truth becomes more complicated than ever before. The popularity of the disposition of cases out of trial and the impact of technology and science, both interrelated and focused on the efficiency of the criminal justice system, paradoxically make the quest for the truth easier and faster, but also more prone to errors. Moreover, the new technologies allowing evidence gathering have become a vital threat to the right to privacy. Finding solutions to these challenges necessitates dialogue including various stakeholders and free of the penal populism that has recently dominated the legal discourse.
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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".