Language of Political Campaigns and Politics in Nigeria
Bibliographic record
Abstract
Communication as a complex phenomenon remains vital to a sustenance of relationships and human existence. It is in fact, the oil that lubricates human interactions. Despite this significance, communication is a double-edged sword which can be used either positively or negatively. Boulton (1978, p.41) attested to the negative social intend of language from the perspective of its potential for complexity. She also observed that ‘’language is often used, not to communicate but to deceive. This is often true of political and religious propaganda... .’’ The intention to manipulate people’s mind and thought is symbolically expressed through print and broadcast media particularly, during political campaigns and in the eventual practice of politics in a given society. Using a purposive sampling method , the paper identified 51 samples but analysed 16 political messages and slogans reflected in the print media (billboard and newspaper-paid advertisements) during the 2011 electioneering campaigns in Nigeria. It was observed that man as a political animal engages in the practice of politics as a social and noble activity to express his political agenda either positively or negatively. Therefore, it was recommended that political candidates should endeavour to inform and persuade electorates rather than deceive or merely entertain them.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| 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".