“Influencer marketing is not a way around the law”: Regulatory Compliance and Law Enforcement in the Canadian Social Media Influencer Field
Bibliographic record
Abstract
In 2019, Competition Bureau Canada (“the Bureau”) sent letters to approximately 100 brands and marketing agencies that engage in influencer marketing—advising them to ensure that their marketing practices are in compliance with the law. Against this background, this research uses semi-structured interviews to examine the regulatory compliance efforts of 21 influencer intermediaries who liaise between brands and social media influencers. The research assesses these intermediaries’ disclosure practices together with their thoughts on the Bureau’s targeted outreach and law enforcement in the field. Additionally, the research explores how the intermediaries adjust their regulatory compliance efforts to technological innovation on social media platforms. This research contributes four main findings. First, intermediaries consign regulatory expectations for disclosure to a secondary level of importance when working to meet the needs of brands and influencers. Second, there is limited intermediary knowledge of the Bureau’s legal standing and activities in the Canadian influencer field. Third, there are various algorithmic (e.g., algorithmic deprioritization) and non-algorithmic (e.g., lengthy disclosure processes) challenges to maintaining high standards of compliance in evolving digital environments. Finally, intermediaries have access to forms of social, cultural, and technical power that can be maximized to influence high standards of compliance. This research contributes to a growing body of scholarship focused on the perspectives of professionals who manage influencer marketing collaborations.
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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 | 0.023 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.044 | 0.032 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".