{"id":"W2209323101","doi":"10.2501/jar-2015-007","title":"How Corporate Sponsors Can Optimize the Impact of Their Message Content","year":2015,"lang":"en","type":"article","venue":"Journal of Advertising Research","topic":"Consumer Behavior in Brand Consumption and Identification","field":"Business, Management and Accounting","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Scope (computer science); Business; Marketing; Focus (optics); Product (mathematics); Advertising; Regulatory focus theory; Process (computing); Event (particle physics); Public relations; Political science; Task (project management); Computer science; Economics; Management","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003771571,0.0001108462,0.0002352654,0.0004849354,0.0001784983,0.0005802067,0.0004694348,0.00004634878,0.00007055725],"category_scores_gemma":[0.0007720076,0.00006296291,0.0002060341,0.0005946754,0.0002240888,0.0008062615,0.0001119212,0.0003998897,0.00001469405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001311076,"about_ca_system_score_gemma":0.000228891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006621998,"about_ca_topic_score_gemma":0.00004070684,"domain_scores_codex":[0.9983006,0.0001358496,0.0004110664,0.0001137964,0.000789173,0.0002495725],"domain_scores_gemma":[0.9962583,0.0001501289,0.0007082277,0.0002701918,0.002565575,0.00004753206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004443197,0.001113371,0.4336998,0.0002202224,0.0007220797,0.0002015319,0.002117992,0.001465647,0.136092,0.001753332,0.07101883,0.347152],"study_design_scores_gemma":[0.003139535,0.00009334655,0.9756345,0.0003696299,0.0001181237,0.00009868032,0.006925874,0.002065754,0.002677056,0.002774651,0.005840047,0.0002627737],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952917,0.000514092,0.00009000913,0.003329193,0.0002763169,0.0001719624,0.000003295495,0.000009205155,0.0003142441],"genre_scores_gemma":[0.9986535,0.00006890339,0.000044669,0.0000296878,0.0002298565,0.000002931388,0.000002941469,0.00001478218,0.0009527709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5419347,"threshold_uncertainty_score":0.5594947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3296096255392679,"score_gpt":0.3680889773320979,"score_spread":0.03847935179282996,"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."}}