A Critical Analysis of Attempts to Regulate Native Advertising and Influencer Marketing
Bibliographic record
Abstract
This research critically examines how regulatory bodies in Canada, the United Kingdom, and the United States are responding to native advertising and influencer marketing, two practices that blur the line between digital media content and advertising. Through an examination of regulatory guidelines, documents, and cases from 2010 to 2020, we demonstrate how regulators adhere to a “narrow” regulatory paradigm that the advertising industry itself helped to establish in the early 1900s. Under this paradigm, the only potential problem caused by advertising is an individual consumer misled into purchasing something they would not otherwise. As such, for native advertising and influencer marketing, regulators recommend clear disclosure as the solution. Our synthesis of critical academic literature, however, reveals the wider social and cultural consequences of native advertising and influencer marketing, including the reputation of journalism and further erosion of the public sphere by commercialism, among other issues.
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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 | 0.038 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.026 | 0.070 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.011 |
| 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".