A PROGRAM BASED ON RATIONAL-EMOTIONAL GUIDANCE IN DEVELOPING DIGITAL CITIZENSHIP AND REDUCING EXTREMIST THOUGHTS AMONG UNIVERSITY FEMALE STUDENTS
Bibliographic record
Abstract
The current research aims to investigate the impact of a program based on rational-emotional guidance on the development of digital citizenship and the reduction of extremist thoughts among university female students. The sample was selected from kindergarten female teacher-students at Prince Sattam bin Abdulaziz University in the Kingdom of Saudi Arabia and early childhood education students at Minia University in the Arab Republic of Egypt. The main research sample consisted of 99 university students, with 48 Saudi students and 51 Egyptian students. The following tools were utilized to achieve the research objectives: the Digital Citizenship Scale, the Intellectual Extremism Scale, and a program based on rational-emotional guidance. The research results revealed the impact of the rational-emotional guidance program on the development of digital citizenship within the investigated sample. Statistically significant differences were observed between the pre-and post-measurement means of the digital citizenship scale, favoring the post-measurement. Furthermore, the research results demonstrated the impact of the rational-emotional guidance program in reducing extremist thoughts within the investigated sample. Statistically significant differences were found between the pre-and post-measurement means of the intellectual extremism scale, favoring the pre-measurement. The study concludes by proposing avenues for further research and providing recommendations for future initiatives in this domain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 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".