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 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 | 0.001 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".