Intelligence-Led Mass Screenings for Detection of Perpetrators. Legal Issues and Public Acceptance
Bibliographic record
Abstract
Intelligence-led mass screening (ILMS, mass screening, canvas, dragnet) is one of solutions for detection of perpetrators. ILMS is based on collection and analysis of reference materials from large group of people with similar features as perpetrator. Practice from many countries suggests that this procedure can be effective. Nevertheless mass tests are focused mainly on third parties (non-offenders) and it is controversial from the point of view of privacy protection and some crucial civil liberties and rules of criminal proceeding. The aim of the research was to check a level of social permission for mass screenings and to set the optimal procedure from legal point of view. The first part of research was focused on level of social permission for mass screenings. 800 persons in Poland were surveyed within the study – 385 persons from general public and 415 law students. About 75% of general public and 65% of law students admitted that they will give a consent for taking reference material if they were asked for it (responses in surveyed groups were statistically different; p < 0.01), which means that ILMS is generally accepted by the most of society. Comparative legal research about application of ILMS in criminal proceedings for selected European countries and common law countries was the second part of study. It was revealed that mass screenings in many countries are voluntary (Germany, Netherlands) or are under the suspect sampling regime (USA, England & Wales, New Zealand, Canada). Only in some countries massive collection of reference materials can be compulsory according to provisions of criminal procedure (Austria, Serbia, Italy, Poland, Sweden, Finland).
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".