Knowledge, attitudes, and practices towards personal data protection in social media among Indonesian college students
Bibliographic record
Abstract
The issue of personal data protection on the internet has become a significant cause of concern for the Indonesian Government. Millions of people's data on private information has been leaked and traded on marketplace sites. Anticipating the same incident in the future, the Indonesian Government issued Regulation No. 27 of 2022 on Personal Data Protection. However, not all citizens are aware of the regulation. Taking data from 325 students at universities spread across Indonesia using quantitative analysis of knowledge-attitude-practice, this study shows how there is still a lack of understanding among citizens about personal data protection. It shows that citizens are still not worried about publishing their data on social media. Many respondents would still publish personal data on social media accounts and itt can be assumed that many still do not know the importance of protecting personal data and the possibility of misuse of information shared, and also do not understand the importance of protecting personal data uploaded on social media and the possibility of misuse of information.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".