{"id":"W2005840824","doi":"10.1108/20426761211203247","title":"Macro‐social marketing and social engineering: a systems approach","year":2012,"lang":"en","type":"article","venue":"Journal of Social Marketing","topic":"Service and Product Innovation","field":"Business, Management and Accounting","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social marketing; Public relations; Social change; Marketing; Government (linguistics); Psychological intervention; Flexibility (engineering); Macro; Legislation; Originality; Business; Political science; Economics; Sociology; Psychology; Economic growth; Qualitative research; Social science; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008032243,0.0002003227,0.000366921,0.0002933829,0.0006466478,0.0004202568,0.0001758341,0.00015618,0.00003847092],"category_scores_gemma":[0.0005153969,0.0001982841,0.0001230348,0.0005516142,0.00003214155,0.001429271,0.0001271998,0.0003991161,0.000006781241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000708938,"about_ca_system_score_gemma":0.0000210109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002362593,"about_ca_topic_score_gemma":4.375281e-7,"domain_scores_codex":[0.9982219,0.0001022727,0.0006362119,0.0001409518,0.0004453998,0.0004532458],"domain_scores_gemma":[0.9983533,0.0001481416,0.001029365,0.00004500384,0.0004078546,0.00001632221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006625502,0.001708752,0.3888678,0.03231212,0.002354687,0.00007815136,0.03427298,0.0001386532,0.03494743,0.1750367,0.1439958,0.1796614],"study_design_scores_gemma":[0.001904118,0.00001357401,0.7481283,0.0002188277,0.0004027108,0.00009599145,0.0182875,0.002638802,0.00001873788,0.0001756738,0.2271794,0.0009364398],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9804995,0.0005026732,0.0002152786,0.003420448,0.001528993,0.0001757526,0.000001580373,0.00006211666,0.01359364],"genre_scores_gemma":[0.9683131,0.000005298794,0.0003293418,0.0006862634,0.03054486,0.000004686663,0.000007769728,0.00004250567,0.00006617372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3592604,"threshold_uncertainty_score":0.8085792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01744664063714792,"score_gpt":0.2158281921917702,"score_spread":0.1983815515546223,"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."}}