The Use of Code Switching/Code Mixing on Olúsẹgun Mímíkò’s Political Billboards, Oǹdó State, South-West Nigeria
Bibliographic record
Abstract
In a multilingual society as Oǹdo State where each language uniquely fulfill certain roles and represents different identities, code switching and code mixing are common phenomena used to meet the complex communicative demands of the majority of the people whose competency in English language is relatively low. Some politicians in the State who are aware of this fact resort to the use of code switching and code mixing in political advertising on the billboards. This paper, therefore, examines the use of code switching and code mixing on Olusẹgun Mimiko’s political billboards during the electioneering period of the just concluded October 20, 2012 Governorship election in Oǹdo State. The paper shows that none of his political billboards is anti-opposition, rather, they are used to eulogize him. The study found among other communicative intents, the use of code switching and code mixing in Olusẹgun Mimiko’s political billboards, the need to sell his candidacy and also educating the electorate on where to thumbprint on the ballot paper. Key words: Code switching; Code mixing; Political billboards; Olusẹgun Mimiko; Oǹdo State
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".