MANAGEMENT OF OPEN SOURCE INFORMATION IN THE MANAGEMENT OF CURRENT CYBER THREATS AND WAYS TO FIGHT FRAUD AT FINANCIAL COMPANIES
Bibliographic record
Abstract
Abstract: The multiple ways of accessing the virtual environment are changing, those who access the Internet are changing and the role that the Internet plays in our lives. In 1995, only 1% of the world’s population had access to the Internet. There are now over 4 billion Internet users worldwide and this number is growing. Over time, cyberspace has generated a series of controversies, starting from the difficulty of being given a unanimously accepted definition. At both state and institutional levels, an attempt was made to define this new concept, the results being different and adapted to the specifics of each organization. Thus, in the Cyber Security Strategy of Canada, cyberspace is presented as “the electronic world generated by the interconnection of computer networks”, and in the Cyber Security Strategy of the United Kingdom of Great Britain and Northern Ireland, it is defined as “An interactive domain of digital networks that store, modify and transport data”. Keywords: cyberspace, artificial intelligence (AI), Big Data, COVID-19 pandemic, corporate governance, Open Source Intelligence OSINT, economic perspectives, OSINT type analysis JEL Classification: F3, O3
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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 | 0.018 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".