Character Education in Muslim Families to Counter the Negative Effects of Digital Technology in the Era of Industry 4.0
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
Objective: This study examines the methods applied by Muslim families in dealing with the impact of technological advances on early childhood in the Industrial 4.0 era, especially in the Surakarta area. Theoretical framework: This research is based on the theory of the social impact of technology and childcare in the Muslim family environment, highlighting the importance of the role of the family in shaping behavior and fortifying children from the negative impacts of technology. Literature review: discusses the influence of technology on early childhood development, the role of parents in religious value-based parenting, and strategies that can be applied in dealing with technological developments in the digital era. Methods: This study uses a descriptive qualitative method with the stages of data reduction, data presentation, and conclusion drawn, through observation and interviews with 10 Muslim families in Surakarta. Results: This study shows that Muslim families apply various methods such as preventive measures, supervision of technology use, free children to play outside with peers, being selective in choosing appropriate applications for children, providing examples of good behavior in the use of technology, and limiting the time of use of technology for children. Implication: this research highlights the importance of the active role of the family in accompanying and directing children in using technology wisely to minimize its negative impacts. Novelty: this research lies in its specific focus on the practice of raising Muslim families in the Industrial 4.0 era in the local context of Surakarta, as well as on the identification of concrete methods applied by parents in dealing with digital challenges in early childhood.
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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 | 0.001 |
| 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