{"id":"W2891534393","doi":"10.1287/mksc.2014.0867","title":"Untangling Searchable and Experiential Quality Responses to Counterfeits","year":2014,"lang":"en","type":"preprint","venue":"Marketing Science","topic":"Economic Growth and Development","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Counterfeit; Monopoly; Intellectual property; Quality (philosophy); Product (mathematics); Business; Experiential learning; Competition (biology); Reputation; Enforcement; Product differentiation; Cournot competition; Industrial organization; Advertising; Microeconomics; Marketing; Economics; Law","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":[0.002524276,0.0003055344,0.0003554019,0.000784093,0.0004729482,0.002130506,0.0006945268,0.0009256066,0.006009939],"category_scores_gemma":[0.01513749,0.0002030282,0.0004605756,0.0003192078,0.003218064,0.00411592,0.001914626,0.001188141,0.0002559242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009770073,"about_ca_system_score_gemma":0.0005299066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006509533,"about_ca_topic_score_gemma":0.0006480727,"domain_scores_codex":[0.9985836,0.0004517164,0.00007727846,0.0002645156,0.0003200969,0.0003028438],"domain_scores_gemma":[0.983739,0.00690679,0.005601301,0.0017232,0.001063519,0.0009661898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.001221072,0.00118334,0.1679776,0.0006682492,0.0002823319,0.001264176,0.005505528,0.0379495,0.05663462,0.596858,0.0009113324,0.1295444],"study_design_scores_gemma":[0.0001403527,0.001135315,0.2232084,0.0001488693,0.0001549834,0.0005519969,0.006701776,0.09576271,0.0136795,0.6530489,0.005332312,0.0001348185],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9236031,0.0003151942,0.04483939,0.001187411,0.0000251721,0.00006680257,0.0000743825,0.00003214943,0.02985629],"genre_scores_gemma":[0.9981037,0.00006417384,0.001024043,0.00006021157,0.000009502935,0.0000103217,0.00001277108,0.000003972566,0.0007111169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006009939,"threshold_uncertainty_score":0.02010524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02893884448114592,"score_gpt":0.3115364841307711,"score_spread":0.2825976396496252,"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."}}