{"id":"W4392876560","doi":"10.32920/25417210","title":"#sponsored on Instagram: Analyzing Influencers’ Content With Luxury Fashion Brands","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Toronto Metropolitan University","funders":"","keywords":"Influencer marketing; Normative; Presentation (obstetrics); Macro; Diversity (politics); Inclusion (mineral); Sociology; Advertising; Representation (politics); Content (measure theory); Content analysis; Through-the-lens metering; Ideal (ethics); Social media; Marketing; Lens (geology); Business; Political science; Computer science; Social science; Relationship marketing; Engineering; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0009695247,0.0002851792,0.0001735422,0.00236682,0.00134334,0.002957529,0.000323674,0.0004453458,0.008633107],"category_scores_gemma":[0.005415575,0.0001284462,0.0001965873,0.002628816,0.001106666,0.002555634,0.001533218,0.0004501686,0.001613742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001185428,"about_ca_system_score_gemma":0.0005480259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00737632,"about_ca_topic_score_gemma":0.01315304,"domain_scores_codex":[0.9990312,0.0004058972,0.00003003287,0.0001112919,0.0003028094,0.0001187492],"domain_scores_gemma":[0.9961907,0.002483674,0.0003317039,0.0003604697,0.000437681,0.0001958002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.000623295,0.0002123679,0.1328634,0.0006906402,0.00005840198,0.002078104,0.5469256,0.0004371861,0.03381285,0.02512004,0.02168727,0.2354907],"study_design_scores_gemma":[0.00001972555,0.0002571978,0.2744002,0.0002650877,0.00007570856,0.001658136,0.3566186,0.005515989,0.02382147,0.006127682,0.3310944,0.0001455796],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9464888,0.0001871627,0.004537557,0.0005365653,0.00006475108,0.0001114016,0.001278454,0.0003131739,0.04648212],"genre_scores_gemma":[0.9671911,0.0002369113,0.005802003,0.000142027,0.00009000314,0.0001256766,0.001565999,0.0004205597,0.02442569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008633107,"threshold_uncertainty_score":0.0288806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05265678648968718,"score_gpt":0.3138630367865052,"score_spread":0.261206250296818,"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."}}