Bibliographic record
Abstract
<p>Social media has become an integral part of our daily life encapsulating time and place, creating new relations and fostering old ones not only on an individual level but also on social and global ones. This revolution in human interaction was led by the introduction of Facebook in 2004 that was followed by other social media platforms such as Twitter and Instegram. This electronic revolution swept over to reach mobile phones and to introduce new platforms such as WhatsApp and Viber. The present study investigated attitudes and views towards the use of social media in promoting Islam. A random sample of Facebook users was asked to fill in a questionnaire that tackled questions related to their attitudes towards the role of social media in promoting Islam, the linguistic influence of the social media on their English language skills when talking about Islam and the most preferred social media platform. . Respondents were then classified according to education and gender. The study revealed that the social media have affected the way the other is addressed when discussing Islamic topics. Despite some negative stands, the positive attitudes towards social media in promoting Islam prevailed. The views were influenced by the respondents’ age, gender and education. The linguistic influence of the social media on developing English skills was viewed positively. The Facebook was the most preferred social media platform. Further research is recommended on the interrelationships between social factors and views of social media. Code-switching among social media users and the effect on Arabic might be also investigated.</p>
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".