Self-Profiling and Agenda Marketing in Buhari’s May 29, 2018 Broadcast and Adjoining Pronouncements
Bibliographic record
Abstract
Considering that the discursive structures in political discourse often manifest the ideological properties of language and the choices that speakers make from a number of alternative words are self-serving, this paper evaluates the Nigerian President, Muhammadu Buhari’s linguistic strategies of self-profiling and political agenda marketing. Data were excerpted from Buhari’s May 29, 2018 Democracy Day broadcast, “The-Not-Too-Young-to-Run-Bill” and his Press Statement announcing a posthumous Award to the winner of June 12, 1993 Presidential Election and the declaration of June 12 as Democracy Day in Nigeria. The study, based on Critical Discourse Analysis, revealed that self-profiling and agenda marketing in these texts manifested language ideologies which were instantiated through the co-option of critical stakeholders, performance profiling and agenda-setting which were aimed at halting the low performance rating of the president but to earn him re-election in 2019. Buhari’s pronouncements and, by extension, his ideology, were political gimmicks aimed at enlisting the support of Nigerians and hoodwink them into giving him a fresh mandate. The study’s interrogation of Buhari’s pre-election pronouncements could guide the public in their reaction to the ideologies of politicians.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".