Lexicalisation in Media Representation of the 2003 and 2007 General Elections in Nigeria
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Some of the previous studies on media representation of elections in Nigeria only provided impressive theoretical and critical analyses of ideology. Not much has been done to show how such ideology(ies) could be lexically accounted for. Given the fact that ideologies are very crucial in elections, this study investigates the ways the cover stories in two Nigerian news magazines, Tell and The News, lexically, express the ideological pursuits of social actors in the 2003 and 2007 general elections in Nigeria. This paper adopts Fairclough theoretical model on ‘wording’ which is equivalent to Halliday’s theoretical model on ‘lexicalisation’. In the analysis, we observe that both magazines used linguistic tools to represent their ideological affiliations, that is, election in Nigeria is a dirty game and politicians are insincere. Lexicalisation and intertextuality intermingled to depict contextual lexical choices.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it