Briefing: Why Goodluck Jonathan lost the Nigerian presidential election of 2015
Bibliographic record
Abstract
“A do or die affair” is a common description of Nigerian elections, which underlines the competitiveness and acrimony that characterize the quadrennial political ritual. The recently concluded 2015 general elections were fiercer than most, with expectations that they would end in a contentious stalemate at best, and engulf the country in violent political crisis at worst. Many people stockpiled food, the affluent and many expatriate workers took strategically timed holidays abroad, and Nigerians in regions of the country distant from their birthplaces sent their families home in expectation of a prolonged post-election crisis. Yet, in the end, the actual conduct and outcome of the elections defied expectations. Not only did Nigeria conduct its most credible and transparent elections since independence with minimal violence but, for the first time in the country's history, an opposition party – the All Progressives Congress (APC) – defeated an entrenched ruling party (the Peoples' Democratic Party, PDP). The peaceful and credible conduct of these polls has set Nigeria on a trajectory towards consolidating its democracy, transitioning from a largely unstable and expedient experiment in 1999 to the realm of political maturity. This briefing discusses how this was achieved despite the challenging context.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".