Gen Z Muslims, Social Contestation, and Digital Citizenship in Indonesia
Bibliographic record
Abstract
This study aims to analyze the contestation, patterns, and forms of social movements among Gen Z Muslims in the era of digital citizenship. In this era, social movements are shaped by issue-based networks, and the key actors driving this transformation are Gen Z activists—particularly Muslim university students affiliated with Islamic student organizations. This article focuses on two main objectives: (1) to analyze the social contestation of Gen Z Muslim movements in the digital citizenship era and (2) to examine the patterns and forms of these movements in relation to their religious identity. This study employs a qualitative research method, with research subjects drawn from Islamic student organizations in Bandung and Pangkalpinang. Data collection techniques include: first, interviews, which were conducted to explore participants' perspectives on the ideology and activities of their movements; second, participatory observation, used to understand internal dynamics, patterns of interaction, and activism practices among Gen Z Muslims; and third, social media observation, utilized to analyze datasets from online platforms. The findings reveal two main categories of Gen Z Muslim social movements in the digital citizenship era: moderate and non-moderate. Moderate movements primarily focus their digital activism on issues of religious tolerance, inclusivity, interfaith dialogue, social justice, gender equality, and minority rights. In contrast, non-moderate movements center their activism on exclusive and conservative religious discourse, often using hashtags such as #Khilafah and #Hijrah as part of their outreach. These movements also tend to idolize figures advocating for religious purification and hijrah (religious migration). Furthermore, the patterns and forms of Gen Z Muslim social movements in the digital citizenship era are characterized by two key practices: volunteerism and crowdfunding, both of which serve as mechanisms for social engagement and mobilization.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".