Modern Means of Evidence Collection and their Effects on the Accused Privacy: The US Law
Bibliographic record
Abstract
The objective of this article is to discuss modern means of evidence collection by the enforcement agencies and their effects on the accused privacy under the United States’ law. Focus of this article is on the modern means of evidence collection such as electronic surveillance, wiretapping and technology eavesdropping, among others. In the age of modern technology, the objective of revealing the truth and instituting justice has encouraged those with an interest in matters of criminal justice to use modern means beside or instead of the conventional means of evidence collection. Resorting to modern means is premised on the need for criminal proceedings to reflect the circumstances and level of progress of the society where it has been taken. The main problem here however is that there is a possibility of the law enforcement interest in prosecution to be favored and the accused rights to be underrated. We found that at the US federal level, the accused’s privacy right is one of the rights included in the Bill of Rights in 1791 (Fourth Amendment) and supported by many case-law. The article adopts a legal analysis approach which is an accepted form of a qualitative method in social science research.
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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 | 0.051 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.009 | 0.048 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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".