A Multimodal Discourse Study of Visual Images in Select Online News Discourse on the 2023 General Elections in Nigeria
Bibliographic record
Abstract
This multimodal discourse study examines visual images in selected online news discourse on the 2023 presidential elections in Nigeria to identify the various meanings which the images have been used to communicate.Two online newspapers, namely, Vanguard and Business Day served as the sources of data.Drawing insights from Kress & Leeuwen's (2006) Visual Grammar Theory, ten images (five from each newspaper) were purposively sampled and subjected to critical analysis using four key components (participants, representation, interaction and composition) of the theory.The results showed that the analyzed visual images are representative of the major presidential candidates' political, religious, and cultural affiliations; voters' religious and cultural orientation; voters' patience and tenacity in exercising their right to vote; the inadequate electoral system; the serenity and tranquility observed in certain polling locations; the presence of military personnel; and the millions of naira lost during election.Furthermore, the visual depictions explicitly summarized what was stated in writing and speaking.The findings corroborate the visual grammar theory and underline the importance of visual images as semiotic resources in transmitting various meanings.
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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 | 0.006 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".