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Record W2920212827 · doi:10.5539/elt.v12n4p7

Transitivity Processes in President Buhari’s ‘My Covenant With Nigerians’

2019· article· en· W2920212827 on OpenAlexvenueno aff
Isaiah I. Agbo, Festus U. Ngwoke, Blessing U. Ijem

Bibliographic record

VenueEnglish Language Teaching · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologyPsychologyVictoryNigeriansPoliticsSociologySocial psychologyLinguisticsLawPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.008
GPT teacher head0.229
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2019
Admission routes1
Has abstractyes

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