Transitivity Processes in President Buhari’s ‘My Covenant With Nigerians’
Bibliographic record
Abstract
Politics and politicking in Nigeria has assumed a considerably new dimension. Actors articulate their ideology and programmes, and construct their subjects and experiences in diverse linguistic processes with a view to achieving political victory. This paper examines clause structures of President Buhari’s My Covenant with Nigerians to reveal the transitivity processes employed by the President in this famous campaign speech in 2015 presidential election. This study utilized Transitivity Processes, which is rooted in Halliday’s (1985) Systemic Functional Grammar, in order to uncover different process types and main participants in the speech, and to explain the functions which these processes perform in the speech in helping the speaker to convey his ideology to Nigerians and convince them to rally support for him. Specifically, objective of this study is the uncover transitivity process types in the speech, their frequency, function and ideological underpinnings. The study reveals that President Muhammadu Buhari utilized mental and verbal processes perception, affection, cognition and volition, and verbal process of saying to appeal to the masses, and to commit himself to serve Nigerians. He equally used material and relational processes to encode his ideology, persuade the people and achieve political victory.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".