Trends and patterns of terrorist attacks targeting the police in Nigeria, 2009–2022
Bibliographic record
Abstract
In Nigeria, terrorist attacks targeting police officers and police facilities have continued to gain momentum since 2009. However, in spite of the intensity of the problem, it is yet to command tangible scholarly attention. Against this background, this study investigates the trends and patterns of terrorist attacks targeted against the police in Nigeria between 2009 and 2022. An exploratory research design was adopted, and data were principally sourced through the content analysis of a corpus of two purposively selected Nigerian national newspapers’ coverage of the recorded incidents of terrorist attacks that were directed at police officers. The results showed that 455 cases of such attacks were recorded between 2009 and 2022. Incidents of terrorist attacks targeting police officers were recorded in nearly all the states of the federation, with Borno State having the largest share (42.9%). Also, the largest single share of the incidents (29.2%) happened in 2021 with the highest percentage of police fatalities (24.8%) occurring in the same year. The majority of the attacks (51.9%) occurred within police stations. Indigenous People of Biafra (32.1%) and Boko Haram (31.2%) were the terrorist groups responsible for most of the attacks on police officers. Terrorist attacks hold multiple serious deleterious consequences for the Nigeria Police Force. Thus, it is important for the law enforcement agency to develop a functional institutional framework through which police officers can be adequately exposed to professional counter-terrorism training and strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".