The de-radicalization, rehabilitation and reintegration project in Nigeria’s counter-terrorism strategy: Operation Safe Corridor in context
Bibliographic record
Abstract
In the years since the emergence of Boko Haram, the terror threat posed by the sect’s violent extremism has remained a challenge for the Nigerian government. The failure to contain it has been attributed to the government’s over-reliance on military strategies. While conventional approaches are useful in weakening the operational capacity of domestic terrorism, they do not provide a long-term solution. The use of military strategies to quell ideological and religious-driven terrorism has proven counterproductive. As a result, scholars and security practitioners have recommended a combination of military and non-military strategies to address insurgency. Non-military strategies include de-radicalization, disarmament, amnesty, indigenous conflict resolution mechanisms, and other soft power measures. In line with this, the Nigerian government adopted ‘Operation Safe Corridor’ in a bid to de-radicalize, rehabilitate and reintegrate former Boko Haram combatants who voluntarily surrender to the government. This article assesses Operation Safe Corridor’s institutional mechanisms as a counter-terrorism strategy in Nigeria. It argues that the lack of a legal framework, issues of public perception and trust and host communities’ reluctance to accept former Boko Haram combatants have undermined successful implementation of the program. It is imperative for the government to address these challenges in order to achieve Operation Safe Corridor’s objectives and ensure successful deradicalization and reintegration of former combatants.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".