The Evolution of Tunisian Salafism after the Revolution: From<i>La Maddhabiyya</i>to Salafi-Malikism
Bibliographic record
Abstract
Abstract What shape does Salafism take in Tunisia after the ban of the Salafi-Jihadi group Ansar al-Shari‘a and the wave of securitization carried out by national authorities? This article argues that a constraining legal context put Salafism's doctrinal rigidity in tension with its survival and ultimately prompted a residual current of Salafi actors to accommodate their stance toward Malikism, the prevalent school ( madhhab ) in the country. This adaptation is at odds with contemporary Salafism, which traditionally dismisses all four law schools ( lā madhabiyya ), rejects their blind imitation ( taqlῑd ), and claims the superiority of the Qur'an, hadith, and consensus of the salaf (pious predecessors) over jurisprudence ( fiqh ). To account for this puzzle, this article scrutinizes the historical development of Salafism and the evolution of its stance toward Malikism across three generational waves. It notably shows how religious securitization associated with the promotion of a “moderate” Islam pushed Salafi actors to redefine their ideology to preserve their preaching and teaching activities. We call Salafi-Malikism the outcome of this adaptive strategy. Drawing on the Tunisian case, we argue that, despite its purist claims, Salafism is not an immutable religious current, but can take different trajectories to survive in constraining environments.
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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 | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".