Problems of implementing international digitalisation standards of criminal investigation
Bibliographic record
Abstract
The study consisted of identifying the existing problems in the implementation of international standards of digitization of criminal investigation at the legislative level. The research was carried out in stages as a summary, based on the logic of presentation of the material, to achieve and meet the objectives set out in the article. The method of direct observation, the method of comparison and analysis of the content of the documents, the method of systemic and pragmatic approach were used. The key results of the study were the analysis of the experience of implementing digital standards in forensic activities in the United States, Canada, Great Britain, Denmark, England, Austria, Estonia, and Ukraine. It is concluded that the problems that exist in the implementation of these standards, were identified from the criteria of evaluation of the efficiency and capacity of digital data processing by the agencies involved in the criminal investigation. In addition, the problems and difficulties faced by the authorities in implementing existing international digitization standards, indicate the need for comprehensive measures to organize criminal investigations. To overcome them, appropriate measures must be taken in the field of legislative changes.
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 | 0.117 | 0.179 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".