SOCIAL MEDIA AS AN EFFECTIVE TOOL FOR IDENTIFYING, SOLVING AND INVESTIGATING CRIMES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The article substantiates promising directions of using information obtained from a social network (digital footprint) in order to identify, disclose and investigate crimes, and provides the experience of Canada and Russia. The author argues that the information contained on personal pages of social networks allows to identify the identity of the suspect/accused/witness/victim, to find accomplices and tools of the crime, to identify important circumstances of importance for the criminal case. The author of the article studied a scientific method for diagnosing a person’s personality traits, which allows for the creation of a psychological profile and the prediction of future behavior. The method involves profiling (profiling) a person and studying authentication systems based on artificial intelligence, which provide access to the entire database of a specific account, allowing for the evaluation of statistics (such as the number of page visits, account activity, and more) and the creation of a comprehensive user database.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it