Silencing Boko Haram: Mobile Phone Blackout and Counterinsurgency in Nigeria’s Northeast region
Bibliographic record
Abstract
In the summer of 2013, the Nigerian military, as part of its counterinsurgency operations against Boko Haram insurgents, shut down GSM mobile telephony in three northeast states – Adamawa, Borno and Yobe. This article explores the rationale, impact and citizens’ opinion of the mobile phone blackout. It draws on focus group discussions with local opinion leaders and in-depth personal interviews with military and security insiders, as well as data of Boko Haram incidences before, during and after the blackout from military sources and conflict databases. It argues that, although the mobile phone shutdown was ‘successful’ from a military- tactical point of view, it angered citizens and engendered negative opinions toward the state and new emergency policies. While citizens developed various coping and circumventing strategies, Boko Haram evolved from an open network model of insurgency to a closed centralized system, shifting the center of its operations to the Sambisa Forest. This fundamentally changed the dynamics of the conflict. The shutdown demonstrated, among others, that while ICTs serve various desirable purposes for developing states, they will be jettisoned when their use challenges the state’s legitimacy and raison d'être, but not without consequences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".