{"id":"W4391594745","doi":"10.32920/25176287","title":"“Influencer marketing is not a way around the law”: Regulatory Compliance and Law Enforcement in the Canadian Social Media Influencer Field","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Centre for Social Innovation; McMaster University; York University","funders":"York University","keywords":"Influencer marketing; Intermediary; Enforcement; Outreach; Compliance (psychology); Scholarship; Public relations; Business; Law enforcement; Social media; Marketing; Competition (biology); Political science; Law; Relationship marketing; Marketing management; Psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02316325,0.0004158898,0.0006069848,0.003966358,0.04365008,0.01254535,0.003556015,0.003372156,0.004439134],"category_scores_gemma":[0.04082513,0.000713253,0.0004079443,0.004477452,0.03223844,0.004194559,0.007355502,0.005591945,0.000254051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1210773,"about_ca_system_score_gemma":0.1939875,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9741911,"about_ca_topic_score_gemma":0.9803498,"domain_scores_codex":[0.9726018,0.006563537,0.0006186098,0.002464208,0.0118691,0.005882779],"domain_scores_gemma":[0.9602915,0.0163533,0.006011276,0.001397753,0.009069229,0.006876999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00006496345,0.0002068302,0.06514989,0.0001878729,0.00003022349,0.001056939,0.8400469,0.000131112,0.000780435,0.03870309,0.00999683,0.04364493],"study_design_scores_gemma":[0.00002371739,0.00007841649,0.1210862,0.0004620575,0.00004105059,0.0002091049,0.7764735,0.0008913422,0.0006033012,0.002997712,0.09697808,0.0001556089],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8884148,0.002057286,0.001504749,0.03347221,0.0001687993,0.0002423803,0.0001089902,0.00004835066,0.07398251],"genre_scores_gemma":[0.9908903,0.0007036559,0.0003845093,0.00254215,0.00002765825,0.0000270343,0.00004052705,0.00002085575,0.005363245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1210773,"threshold_uncertainty_score":0.8784813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03186351317316094,"score_gpt":0.2666527222907835,"score_spread":0.2347892091176226,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}