{"id":"W3159915317","doi":"10.3386/w23046","title":"Exit, Tweets and Loyalty","year":2017,"lang":"en","type":"preprint","venue":"National Bureau of Economic Research","topic":"Aviation Industry Analysis and Trends","field":"Economics, Econometrics and Finance","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Loyalty; Advertising; Computer science; World Wide Web; Business; History; Marketing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009754968,0.0001355593,0.0002940045,0.001230338,0.0008375795,0.002044332,0.0002228783,0.0008210658,0.006016959],"category_scores_gemma":[0.01584061,0.000133456,0.0002354378,0.001619835,0.001518401,0.00210414,0.001016553,0.001142263,0.0004301378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000995524,"about_ca_system_score_gemma":0.0005508444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01065329,"about_ca_topic_score_gemma":0.01090298,"domain_scores_codex":[0.9993836,0.0001601182,0.00005062653,0.0001113191,0.00013296,0.000161343],"domain_scores_gemma":[0.9773144,0.01157661,0.008128542,0.0005465711,0.0007398393,0.001694063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002268604,0.0001516854,0.9411306,0.000104419,0.0001140096,0.000221868,0.002530301,0.001312297,0.0003003652,0.01932381,0.00658578,0.02799798],"study_design_scores_gemma":[0.00002220203,0.0001288313,0.9639446,0.00007709878,0.00005767151,0.00022044,0.003884933,0.0035102,0.0003704942,0.01922238,0.008519526,0.00004166607],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9791667,0.00118587,0.001219843,0.004201254,0.0000474066,0.00001525151,0.001092799,0.00002315997,0.01304773],"genre_scores_gemma":[0.9978825,0.0001836719,0.00009939531,0.0001866114,0.00006347204,0.00000543341,0.0002866764,0.000003752635,0.001288477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01065329,"threshold_uncertainty_score":0.02118254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5350326381457466,"score_gpt":0.5080492751977758,"score_spread":0.02698336294797088,"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."}}