COVID-19 and Cybersecurity: Finally, an Opportunity to Disrupt?
Bibliographic record
Abstract
COVID-19 has challenged cybersecurity to meet the ultimate need of guaranteeing the privacy and security of human beings. Although personal and sensitive health data are needed to better understand, detect, and control the disease, many related cybersecurity challenges and vulnerabilities require further analysis and proper discussion. The aims of this viewpoint are to explore the consequences of COVID-19 on cybersecurity and health care as well as to foster awareness regarding the need for a change in paradigm on how cybersecurity is approached. Education and information technology literacy are important when they are suitably provided; however, they are certainly not a complete solution. Disruption needs to occur at the core of human-device interactions. Building trust, providing novel means to interact with technology (eg, digital humans), and supporting people-the most important cybersecurity asset-are only some of the recommendations for a more human and resilient approach to cybersecurity, during or after the pandemic.
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.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.022 | 0.029 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".