A Critical Analysis of Attempts to Regulate Native Advertising and Influencer Marketing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it