Bibliographic record
Abstract
Abstract This book provides an insider–outsider analysis of the #BringBackOurGirls (#BBOG) movement. The #BBOG was formed through a coalition of elite women and middle-class allies to advocate for the rescue of over 200 high-school girls kidnapped by Boko Haram in 2014. The book argues that the #BBOG is a global leader in ‘lives matter’ advocacy and a new episode in African women-led rights movements. Based on multi-year empirical research, the book demonstrates how the #BBOG transformed the Chibok kidnapping into an international cause and a social problem in a sociological sense and inadvertently created a social problem industry. This work is an in-depth engagement with the organizational structure, decision-making, repertoire of protest, framing, internal dynamics, and divisions within the #BBOG. The #BBOG is far more than a social media phenomenon: the movement deploys a hybridized communication process, which seamlessly combines the social media with traditional media. The BBOG was enmeshed in toxic presidential politics and an ideational battle with the military and two successive Nigerian governments regarding the rescue of the Chibok girls. State repression against the #BBOG and the movement’s outcomes and impact are explicated. The #BBOG contributed to the first electoral defeat of an incumbent president in Nigeria’s history. The #BBOG experience speaks to the texture of the African state, its military architecture, party politics, and challenges to human rights advocacy. The findings have implications for peace and security in Africa, the war against terrorism in the Lake Chad Basin, perpetuation of social problems, and social movement outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".