{"id":"W2012858535","doi":"10.1016/j.intmar.2013.06.001","title":"Thematic Discrepancy Analysis: A Method to Gain Insights into Lurkers and Test for Non-Response Bias","year":2013,"lang":"en","type":"article","venue":"Journal of Interactive Marketing","topic":"Consumer Behavior in Brand Consumption and Identification","field":"Business, Management and Accounting","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Test (biology); Thematic analysis; Thematic map; Psychology; Computer science; Geography; Sociology; Cartography; Qualitative research","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":[0.002620947,0.0001501643,0.0003266422,0.001006778,0.0001627897,0.0005856628,0.0001698615,0.00003439307,0.0002213353],"category_scores_gemma":[0.005863405,0.0001165111,0.0002172835,0.0005541209,0.00002176987,0.001269259,0.0000842442,0.0001402328,0.0000376799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005393873,"about_ca_system_score_gemma":0.00001945661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001207072,"about_ca_topic_score_gemma":0.0000418013,"domain_scores_codex":[0.9987142,0.000132208,0.000622745,0.0001922659,0.0001945146,0.0001440492],"domain_scores_gemma":[0.995055,0.003110636,0.0008585775,0.0001419762,0.0007968451,0.00003696209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004986247,0.0004408363,0.1296868,0.0004422622,0.001326685,0.0000123653,0.003090339,0.00003269413,0.1279659,0.0001010595,0.004648438,0.7272664],"study_design_scores_gemma":[0.001063903,0.00003105627,0.9642274,0.0005801219,0.001238022,0.00001122251,0.008466993,0.01917028,0.0007233464,0.0005587935,0.003628423,0.0003004475],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98081,0.00006012111,0.01689922,0.001459919,0.0001696047,0.0003986493,7.142018e-7,0.00001216713,0.0001896277],"genre_scores_gemma":[0.9895093,0.000005510683,0.009624047,0.0003459098,0.0001595793,0.00005971566,0.000002278975,0.00001557376,0.0002781199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8345406,"threshold_uncertainty_score":0.7019467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02590550873841705,"score_gpt":0.3122672512363366,"score_spread":0.2863617424979195,"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."}}