{"id":"W3170973736","doi":"10.1108/ijsms-08-2020-0150","title":"Season ticket holder segmentation in professional sports: an application of the sports relationship marketing model","year":2021,"lang":"en","type":"article","venue":"International Journal of Sports Marketing and Sponsorship","topic":"Sports, Gender, and Society","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nipissing University; University of Alberta; University of Guelph; Mount Royal University","funders":"","keywords":"Ticket; Market segmentation; Marketing; Context (archaeology); Business; Advertising; Sports marketing; Club; Public relations; Relationship marketing; Computer science; Political science; Geography; Marketing management","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":[0.01616837,0.0001433021,0.0002334317,0.0001626986,0.0002657789,0.00006626021,0.0003037901,0.0001667431,0.0001393759],"category_scores_gemma":[0.002338167,0.0001252218,0.0001544109,0.0003259727,0.0001729005,0.0004062271,0.00006037973,0.0003946759,1.576202e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000159082,"about_ca_system_score_gemma":0.0007853294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003405965,"about_ca_topic_score_gemma":0.0001275476,"domain_scores_codex":[0.9959387,0.0009448346,0.0008600597,0.0002593852,0.001767248,0.0002297751],"domain_scores_gemma":[0.9971139,0.0007587922,0.001050226,0.0001719752,0.0007724037,0.0001326659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006526571,0.000167881,0.9784451,0.00002958052,0.00002120975,0.00006711872,0.01145877,0.000681069,0.0001526036,0.002012059,0.0001495624,0.006162456],"study_design_scores_gemma":[0.0004393898,0.000004222645,0.9724471,0.0005301092,0.00003224514,0.00005484897,0.02217313,0.001216428,0.00006948711,0.002023411,0.0008723736,0.0001372389],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939129,0.0002434385,0.0002346392,0.001686233,0.001018568,0.0001755887,0.000006739701,0.00001319244,0.002708647],"genre_scores_gemma":[0.9936454,0.0003329839,0.001783484,0.0001779244,0.0003126348,0.000005716084,0.00001763305,0.00001641449,0.003707804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01383021,"threshold_uncertainty_score":0.5603667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02993811942625045,"score_gpt":0.3266461784693767,"score_spread":0.2967080590431262,"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."}}