Critical Metaphor Analysis of Political Discourse in Nigeria
Bibliographic record
Abstract
Metaphor is an important figure of speech copiously deployed in political discourse. In this study, we adopted the framework of Charteris-Black’s (2004) Critical Metaphor Analysis (CMA) which derives from Critical Discourse Analysis (CDA).This framework is interested in exploring the implicit intentions of language users, the ideological configurations and the hidden power relations within socio-political and cultural contexts. It captures the ideological and conceptual nature of metaphor, and transmits truth alive into the hearts of the people by passion. The thrust of this study is the identification, analysis and interpretation of the ideological and conceptual metaphors in the speeches we studied that create a particular linguistic style, conceptualize the speakers’ experiences and transmit their ideologies for rhetoric and argumentation purposes. The corpus of this study is limited to the political speeches of Brigadier Sani Abacha in 1984 and 1993, General Ibrahim Babangida in 1985 and 1993, M.K.O. Abiola in 1993 and 1994, and Goodluck Ebele Jonathan in 2013. The study reveals that the speakers use metaphors as tools to enact power and wield influence on their audience. There is further the use of metaphors for the purpose of argumentation thereby promoting self-ideologies and power asymmetric. Furthermore, the study shows that the speakers in the speeches we analysed use metaphors as a strategy to identify with the people so as to create a bond between them. Finally, our speakers use metaphors to manipulate their audience both mentally and conceptually, polarize between them and the conceived enemies, and dominate their audience; and conceal and conceptualize experience in order to reframe realities to suit their interests.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| 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".