A Multimodal Analysis of Differences Between TV Commercials and Press Advertisements: A Discoursal Study of Persuasion-Seeking Strategies in the Mass Media
Bibliographic record
Abstract
This study reports the findings of a multimodal analysis seeking to find the differences between TV commercials and newspapers advertisements in the application of strategies which make those ads more and more persuasive. Regarding such strategies effort is made to find out which ones are more convincing and appealing to their receivers. The tools used in this study involved 40 tape-recorded TV commercials and 40 ads taken from two Iranian newspapers, Tehran Times and Iran Daily which are published in English as well as the American magazine of Newsweek. The persuasion techniques of each medium were first specified separately and then compared with the techniques used by the others. The main finding of the study was that strategies applied by producers of TV commercials are much more persuasive and impressive than those used by suppliers of advertisements in the press.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".