{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001719318,0.0001565033,0.000425199,0.0001341437,0.0002255958,0.00003736494,0.0005704768,0.0000849175,0.0002526208],"category_scores_gemma":[0.0003187133,0.0001454593,0.0002142432,0.0005325815,0.0001446348,0.0001570128,0.0001698375,0.001571478,0.00001867888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002043488,"about_ca_system_score_gemma":0.0009448878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000673282,"about_ca_topic_score_gemma":0.00013525,"domain_scores_codex":[0.9972393,0.0002679301,0.0005694907,0.0003433695,0.0003137157,0.001266187],"domain_scores_gemma":[0.9988185,0.00008253557,0.0004352539,0.0003669154,0.0001061451,0.0001906351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.001580662,0.00104618,0.2762492,0.0001398562,0.0639834,0.0004654135,0.1037812,0.002118158,0.08337421,0.3190301,0.0101588,0.1380728],"study_design_scores_gemma":[0.02362045,0.004604345,0.09266653,0.0006734914,0.04151168,0.003998789,0.3145266,0.2306765,0.002332652,0.2177799,0.06148425,0.006124733],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9783981,0.002121909,0.01582902,0.002530607,0.0002213518,0.0001046394,0.00003549353,0.00002646799,0.0007323924],"genre_scores_gemma":[0.9968985,0.000289395,0.0003728397,0.001945985,0.0002645599,8.47335e-7,0.0000502705,0.00002171366,0.0001558949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2285583,"threshold_uncertainty_score":0.6827378,"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."}}