A Critical Discourse Analysis of the State of Emergency Speech Declared by Olusegun Obasanjo in 2004
Bibliographic record
Abstract
Several studies have analysed the way presidential/political speeches are deployed to capture the ideologies of the speech maker. Many as these studies are, scholars have not attempted a critical discourse analysis of the State of Emergency speech declared by former Nigeria President Olusegun Obasanjo, in May 2004. This study examines the State of Emergency speech with a view to examining the way various linguistic categories are deployed to achieve different functions in the speech. The study deploys Norman Fairclough’s model of critical discourse analysis as theoretical framework. This model is adopted because it provides a platform for the description, interpretation and explanation of text and talk. The data is sourced through the purposive sampling method. This is because the speech is considered as one of those in which Obasanjo’s power consciousness, through his linguistic choices, is enunciated. The study revealed that Obasanjo used nouns, verbs, adverbs, adjectives, collocations and assertions to achieve three main purposes in the text: to justify his decision to declare a state of emergency in Plateau State, to castigate purported culprits and to delegitimise, unequivocally, violence/terrorism. The study reveals that Obasanjo uses language to underscore the need to chart a new course for good governance in the war-torn Pateau State.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".