“Is your manhood failing You?”: persuasion strategies in the audio advertisements of herbal sex-enhancement drugs in Southern Nigeria
Bibliographic record
Abstract
This paper explores of the persuasion strategies employed by advertisers promoting herbal aphrodisiacs within Southern Nigeria. It involved the systematic recording of a dataset comprising 40 advertisements strategically sampled from various locations across the southern region, including markets and car parks. To unravel the strategies in these advertisements, the analytical lens of Simpson’s concept of ‘Reason’ was applied. The identified persuasion strategies embed techniques that advertisers deploy to capture attention and influence the perceptions of target audience. There is the abundant use of problematization of medical conditions, wherein advertisers strategically emphasize the perceived issues or challenges that their products purportedly address; blame-shift hedging is evident, wherein advertisers deflect potential scepticism by attributing certain concerns to external factors, thereby framing their products as the solution rather than the problem. These persuasion strategies are not arbitrary but are carefully tailored based on purportedly researched consumer behaviours. This symbiotic relationship between the strategies employed and the anticipated consumer responses highlights a strategic alignment with the advertisers’ objectives. This study provides a comprehensive examination of the diverse and sophisticated persuasion strategies wielded by advertisers in the promotion of herbal sex enhancement drugs in Southern Nigeria.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".