Textual Organization for Effective and Meaningful Communication: A Focus on the Speeches of Muhammadu Buhari
Bibliographic record
Abstract
Texts are not just texts. Texts are considered texts because of the structure and organization they have. Many linguists do not pay attention to the structure of the text instead attention is paid to the nature of delivery and other aspects of the text. Thereby leaving a vacuum as to what is the internal build of a text. In response to this, the present study presents an analysis of the textual patterns of four of Buhari’s speeches focusing on the textual patterning models of problem to solution, General to Specific and Claim to Counter Claim. The purpose of the study is to unravel the technique behind the arrangement of ideas in the speeches. The study adopts Hoey’s theory of textual patterning as the theoretical framework and reveals amongst others that in the problem to solution model, more problems are presented in Speech A and C which were presented in Nigeria against Speech B and D presented in the United States and in Ethiopia respectively. The problems presented in Speech A and C are local and remote problems related to Nigerians alone while the problems presented in Speech B and D have global links as they affect many countries. The study also discovers that the provision of a futuristic solution in the Problem to Solution Model of Textual Patterning acts as a pointer to the present situation even if it was not stated expressly and the use specificity in the general to specific model acts as a form of reinforcement on the information value of the general statement.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 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".