Social Media Contribution to the Promotion of Digital Citizenship among Female Students at Imam Mohammed bin Saud Islamic University in Riyadh
Bibliographic record
Abstract
The study aimed to identify the degree of social media contribution to reinforcing digital citizenship meaning from the viewpoint of female students at Imam Mohammed bin Saud Islamic University in Riyadh. The study was an attempt to answer the following two questions in order to achieve the objectives of the study: To which extent does SnapChat site reinforce digital citizenship meaning as the female students of Imam Mohammed bin Saud Islamic University in Riyadh understand? What degree of contribution does Twitter site strengthen digital citizenship meaning as the female students of the same University understand? The researcher used the survey descriptive method to answer these questions. The tool of this study was a questionnaire, and the society of the study was the female students of Imam Mohammed bin Saud Islamic University in Riyadh understand and the study was applied on 100 female students. Using the proper statistical tools to analyze the collected data, the study revealed that the SnapChat site as well as the Twitter site contribute to reinforce digital citizenship meaning as the female students of Imam Mohammed bin Saud Islamic University in Riyadh understand. Both sites have added technology skills to the students such as publishing quickly, expression, freedom to express an opinion, and communicate quickly with all over the world. The SnapChat and Twitter have no digital security feature in terms of positioning, ease of penetration and save the private clips. The study concluded with a set of recommendations and suggestions.
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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 | 0.001 | 0.009 |
| 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 | 0.001 | 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".