The Technologies of Remote Communication in The Investigation and Trial and Their Impact on the Requirements of Justice
Bibliographic record
Abstract
Using the remote communication technology in investigation and trial procedures is based on linking the parties of a criminal case in one geographical scope, or in several areas in the same country, or in different regional places among different countries. Therefore, it is imperative to get acquainted with the general rules in remote investigation and trial that have been introduced by criminal legislation to break the traditional general rules of litigation, and to take into account the technological development data in the field of crime detection without prejudice to the rights of the accused or other parties to the criminal case. There is no doubt that the use of audio-visual communication technology will clearly contribute to reducing the financial burdens on the parties of the case, in addition to the legality of these procedures and their impact on the criminal justice system. Consequently, most criminal legislation seeks to include new means and methods for conducting investigations and criminal trial procedures and to create effective litigation procedures in pursuit of achieving justice in its optimal form, especially as technological and technical means are constantly developing, which would necessitate to employ this tremendous development in technological data and modern technology to develop the justice sector.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".