The de-radicalization, rehabilitation and reintegration project in Nigeria’s counter-terrorism strategy: Operation Safe Corridor in context
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it