Bibliographic record
Abstract
This paper is concerned with two questions: What are the master frames of the #BringBackOurGirls (#BBOG) movement? Why did the #BBOG attract significant global attention but achieve only moderate success in its goal – the release of all the school girls kidnapped by Boko Haram in Chibok in April 2014? The paper draws on primary and secondary data to argue that the international attention generated by #BBOG framing had historically specific resonance with local contestations for political power. The reverberation of the framing led to the alienation of key political actors in Nigeria who could have helped achieve the movement’s objective. The involvement of elite women in the movement played a major role in its global popularity but their political activities and loyalties before and during movement activities influenced local perceptions of the movement. The #BBOG’s rhetorical over-reliance on international support for achieving the movement’s objective was a strategic error. The #BBOG experience suggests the need for activists, particularly in the developing world, to recognise the constraints of their political context, work with local actors to achieve objectives, and publicise what ‘international support’ means for movement objectives.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".