Performing Shīʿīsm and Martyrdom: The Place of Religious Songs in the #freeZakzaky Occupy Abuja Movement
Bibliographic record
Abstract
This study examines the transformation of IMN’s religious songs associated with IMN’s religious rituals as a means of framing martyrdom in the face of state suppression; protesting against the secularity of the Nigerian State, and calling for the release of IMN’s leader Sheikh Ibrahim al-Zakzaky during the #FreeZakzaky Occupy Abuja Movement. Why does the IMN incorporate its religious songs into street protests and what roles does it play in performing pain, suffering, and martyrdom in the occupy movement? This study is framed around the theoretical conceptualization of “radicalism” to understand the causes of the IMN’s radical approaches in ideologies, its frequent confrontational protests against the State security apparatus, and the implication for future religious radicalization. The causes of the IMN protests are performed through songs to narrate the Zaria carnage and the State’s violence against the IMN and reenact the religious ideation of martyrdom. The immortalization of martyrs and the religious ideation of achieving martyrdom became a collective identity of performing suffering and death as a religious necessity for IMN’s true followers amid religious repression during the #FreeZakzaky Occupy Abuja Movement. I argue that the performance of the pain and suffering of past and present events will further radicalize members of the IMN, and in the future, there could be a possibility of some of them integrating into a violent jihadi group as a means of self-defense and religious determination against the predominantly Sunni community, and by extension, the Nigerian State.
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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 | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".