Covert Marketing Advertising Law Review Paper
Bibliographic record
Abstract
This paper examines the legal and ethical issues surrounding stealth marketing, especially in the current popular trend of influencer marketing on social media, and its effects on the trust and perception of consumers. The paper explores regulatory frameworks, focusing on the American FTC and the European UCPD, which govern self-regulation and disclosure requirements. This research adopts a literature review of the selected case studies and a review of general findings on advertising transparency, consumers’ behavior, and general cultural responses to covert advertising. The comparative method was used to determine differences between the US state laws and EU laws regarding stealth marketing. The findings indicate that when commercial intent in marketing is concealed, consumer confidence and brand loyalty are compromised. Although younger generations and customers in non-Western countries may tolerate hidden advertising to an extent, integrity is vital for long-term brand development. The study also highlights the potential of AI-supported systems to assist in compliance and increasing transparency in social media advertising. The conclusions indicate that enhanced compliance with the legal provisions on transparency and the implementation of AI monitoring mechanisms in organizations offer much potential to improve consumer confidence and ensure brand responsibility in the global marketplace
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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".