{"id":"W3213776701","doi":"","title":"#BuyNothingDay: Investigating Consumer Restraint Using Hybrid Content Analysis of Twitter Data","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Death Anxiety and Social Exclusion","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Originality; Consumerism; Consumption (sociology); Openness to experience; Content analysis; Value (mathematics); Set (abstract data type); Everyday life; Consumer behaviour; Public relations; Business; Sociology; Marketing; Social psychology; Qualitative research; Psychology; Political science; Social science; Computer science; 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":[0.002142893,0.0002722387,0.0002637501,0.002446769,0.001320162,0.002053871,0.0003454453,0.0005888874,0.003074677],"category_scores_gemma":[0.01250863,0.0001321783,0.0002791763,0.003665963,0.0007652715,0.002605935,0.001484723,0.0006070388,0.001178227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00113251,"about_ca_system_score_gemma":0.0008427885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005634802,"about_ca_topic_score_gemma":0.01243193,"domain_scores_codex":[0.9981574,0.0008469364,0.0001688061,0.0002165848,0.0004725411,0.0001376269],"domain_scores_gemma":[0.9910149,0.005253757,0.001530588,0.0005710586,0.001345416,0.0002843672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008739341,0.0003051145,0.4927716,0.003201178,0.0002103486,0.002085608,0.2036142,0.001173477,0.01969023,0.01246024,0.04829715,0.215317],"study_design_scores_gemma":[0.00002986533,0.0002920034,0.5309135,0.001267395,0.0001448765,0.0006902556,0.2410306,0.009635481,0.009083416,0.006659203,0.200054,0.0001994647],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9536133,0.0005690986,0.006422731,0.003675342,0.0002687524,0.0007639028,0.01623172,0.0001723656,0.01828283],"genre_scores_gemma":[0.9619529,0.0007239368,0.01682863,0.001084434,0.0002446813,0.001794291,0.01059907,0.000155152,0.006616945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005634802,"threshold_uncertainty_score":0.01133281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2076858059429564,"score_gpt":0.3694474794058884,"score_spread":0.161761673462932,"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."}}