The business of ransomware and its effects on business
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
Ransomware is a growing problem that not only affects a company’s financials but also leads to a loss of productivity, which can be difficult to quantify. This criminal extortion preys upon the vulnerability of the average computer user, and Canadian companies are at an increasing risk of experiencing a ransomware attack. The purpose of the current study was to survey a wide range of Canadian businesses about their experiences with ransomware attacks while also gaining insight on this issue with smaller businesses to address a gap in the research. Businesses were questioned about details regarding the ransomware attack(s), which include th following: reactions to the attack, the extent of the damage caused, and whether the business used internal or external IT services. Results reveal critical insights into the prevalence, financial impact, and organizational responses to ransomware attacks. Notably, 29% of businesses reported experiencing at least one ransomware attack, with most cases in Ontario (43%), and 85% of businesses contacted law enforcement following an attack, though satisfaction with police assistance varied. Comparative analysis showed no statistically significant differences by company size in having insurance, contacting police, or ransom demands, suggesting that both small and large companies face similar challenges in ransomware preparedness and response.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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