{"id":"W4396765343","doi":"10.2139/ssrn.4822029","title":"Distilling Brand Alliance Opportunities from Information Networks","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Alliance; Business; Social media; Marketing; Value (mathematics); Brand names; Space (punctuation); Advertising; Computer science; Political science; 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":[0.001163329,0.0003593339,0.0002282236,0.001906663,0.0006891897,0.00336583,0.0004913789,0.0008303761,0.00879701],"category_scores_gemma":[0.01255937,0.0003428272,0.0002914292,0.001645746,0.000777346,0.01135917,0.003433366,0.001334723,0.001040216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005713321,"about_ca_system_score_gemma":0.0005433725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005322234,"about_ca_topic_score_gemma":0.001808682,"domain_scores_codex":[0.9994352,0.0002317122,0.00001909605,0.0001008149,0.0001480601,0.00006504986],"domain_scores_gemma":[0.9931827,0.005340728,0.000440692,0.000449422,0.0003092134,0.0002772141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001311289,0.0008253516,0.03739219,0.0005211239,0.000128101,0.0008190388,0.005633285,0.04036195,0.01279945,0.3959931,0.005893442,0.4983217],"study_design_scores_gemma":[0.00008331721,0.0004074127,0.01038994,0.0002078762,0.000143829,0.0004155693,0.005386682,0.3035071,0.008419613,0.6477753,0.02320494,0.000058272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7625831,0.0008081473,0.1093796,0.002729614,0.0001180506,0.0001190745,0.000418725,0.0003084367,0.1235351],"genre_scores_gemma":[0.9853739,0.0002792008,0.01129439,0.00004995488,0.00002564115,0.00002915026,0.0001295402,0.00002440963,0.002793889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00879701,"threshold_uncertainty_score":0.02942896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01777610247911129,"score_gpt":0.2640966374279504,"score_spread":0.2463205349488391,"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."}}