Pragmatic Analysis of President Muhamadu Buhari’s Cumulative Lockdown Order of the Federal Captital Territory, Ogun and Lagos States on COVID 19 Pandemic at the State House Abuja on Monday, April 27, 2020
Bibliographic record
Abstract
This paper investigates the pragmatic acts of locution, illocution, and perlocution in President Muhamadu Buhari’s cumulative Lockdown order of the Federal Capital Territory, (Abuja), Ogun, and Lagos States during the COVID 19 pandemic on Monday, April 27, 2020. We adopt J. L. Austin’s (1962) and Searle’s (1969) speech act theory, using the illocutionary acts of: expressive, declaratives, assertive, directives, and commissive. The data for this paper are drawn from President Muhammadu Buhari’s speech on COVID 19 on Monday, April 27, 2020. In this study, qualitative research method is adopted; and the descriptive survey method is used for the data analysis. The study reveals that the President used more of assertive speech acts which recorded an overall frequency of 15 and (42%) to affirm, announce, report and state the damaging effect of the pandemic on human lives and economies across the globe, and the measures to be taken in protecting the lives and livelihood of Nigerians. This is followed by expressive and commissive speech acts which recorded a frequency of, 8 and (22%) each and finally directive speech act which has a frequency of 5 and (14%). The perlocutionary effects of the lockdown order on Nigerians are: hope, optimism, compliance and awareness. The percentage and frequency of speech acts are arranged on a table and presented on a pie chart.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".