Framing and stereotyping of two frontline presidential candidates in Nigeria’s 2019 general election: evidence from Nairaland virtual community
Bibliographic record
Abstract
Contributing to the extant literature on media political discourse, this article examines Nigerians’ use of language in framing and stereotyping two frontline presidential candidates, Muhammadu Buhari and Atiku Abubakar, in the 2019 general election. Data were purposively sampled from Nairaland and classified into twelve comment types according to their thematic frames. Following a descriptive analysis, and using van Dijk’s socio-cognitive model of critical discourse studies and Entman’s framing theory, we find that Nairaland netizens’ language practices demonstrate dichotomous framing and stereotyping of a negative “them” and positive “us” through the deployment of multiple discursive strategies: name-calling/abusive labelling, metaphorisation of political actors and pronominal selection. These strategies signify divergent political perceptions about the two candidates and their supporters. We conclude that Nairaland is a sociocultural site that enables Nigerian youth to express their political thoughts in terms of their preferred presidential candidates and political parties.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| 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".