Does Consumer Believe on Advertisers? The Evaluation of Advertising Skepticism in India and China
Bibliographic record
Abstract
Population of India and China consist of 37 percent of the world. The rise in their economic growth creates more potential consumers through excessive advertising – making a person buy things (products) they don’t desire – which to some extent creates skepticism about its credibility. It even makes the consumer skeptic about the valuable information in the message advertised (Calfee & Ringold, 1994). In this article we will investigate the element of skepticism of consumer in advertising in the light of the economics of information (EOI). Based upon the results obtained, through the survey of Chinese and Indian consumers, it can be interpreted that the economical, social, moral as well as personal usefulness and the regulatory aspects of advertising are taken into account. The results of this study would shoulder to determine the scope of advertising in China and India, and will draw the attention of policy makers on consumers’ skeptic behavior in advertising.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".