RELIGION, TECHNOLOGY AND SOCIAL CHANGE: REPRESENTATIONS OF MUSLIM WORLD IN ACADEMIC ANALYSES OF THE ROLE OF SOCIAL MEDIA IN THE ARAB SPRING
Bibliographic record
Abstract
This article deconstructs the dominant constructions and portrayals of the Muslim world in literature on social media and civic engagement in relation to the Arab Spring. A critical reading of literature on social media and ‘Arab Spring’ shows that analyses by Western scholars and commentators are still grounded in ‘modernist dualism’ and orientalist understandings. The article starts by tracing the history of technology to argue that analyses of social media’s educational and civic potential within the Western context in general, is continuation of arguments about earlier technologies in relation to societal development. However, when it comes to analyzing social media and civic engagement particularly in the Muslim world this tendency gets muddled with another well-established trend, that of Orientalism. The overall impact of this tendency results in restricting majority of arguments within the essentialists/determinists paradigm. Such analyses essentialize the technological aspects of social media as universal and constitute the West as civilized, democratic, multicultural, and progressive. On the other hand Muslim world is represented as uncivilized, undemocratic, uncultured, and chained in past traditions. Thus, there is a need for a nuanced understanding of the relationship between the social media and civic engagement in the Muslim world, which can be conceptualized by framing the issues within a postcolonial critique of neoliberal globalization. DOI: 10.15408/tjems.v1i2.1264
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".