COVID-19 and cyber fraud: emerging threats during the pandemic
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
Purpose The emergence of the novel coronavirus (COVID-19) has threatened physical and mental health, and changed the behaviour and decision-making processes of individuals, organisations, and institutions worldwide. As many services move online due to the pandemic, COVID-19-themed cyber fraud is also growing. This article explores cyber fraud victimization and cyber security threats during COVID-19 using psychological and traditional criminological theories. It also provides a COVID-19-themed cyber fraud typology using empirical evidence from institutional and agency reports. Through organizing COVID-19-themed cyber fraud into four different categorizations, we aim to offer classification insights to researchers and industry professionals so that stakeholders can effectively manage emerging cyber fraud risks in our current pandemic. Design/methodology/approach The approach the study take for this conceptual paper is typology.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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