Cybersecurity Vulnerability Behavior Scale in College During the Covid-19 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
The penetration of Indonesian internet users in first quarter of 2020 has increased by 17 percent compared to 2019. Based on Google Consumer Barometer in 2018, many 79% of Internet users in Indonesia use the internet on a daily basis. During the Covid-19 Pandemic, universities had to do Study From Home and Work From Home. This resulted, use of information technology and computers also increasing. This increase will have an impact on the level of cybercrime vulnerability. The scale of cyber vulnerability is needed to measure level of cybersecurity in universities, especially in data managers. There are five scales, Very Safe, Safe, Vulnerable, Very Vulnerable, and Dangerous. Where the scale is used in negative and positive statements. The measurement results show an average value of 3.3 or a vulnerable scale. Total average value of negative statements is 2.53 or scale close to vulnerability. So it is necessary to socialize the importance of cybersecurity to minimize occurrence of cybercrime.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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