{"id":"W3036410797","doi":"10.3389/fpsyg.2020.01307","title":"Motivational Profiling of League of Legends Players","year":2020,"lang":"en","type":"article","venue":"Frontiers in Psychology","topic":"Motivation and Self-Concept in Sports","field":"Psychology","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ontario Institute of Technology","funders":"","keywords":"Psychology; League; Battle; Profiling (computer programming); Social psychology; Vitality; Game play; Screening game; Intrinsic motivation; Cognitive psychology; Game theory; Non-cooperative game; Human–computer interaction; Computer science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002323606,0.0001359226,0.000405959,0.0002424349,0.00001631382,0.000001715951,0.0002710743,0.0002177827,0.001617222],"category_scores_gemma":[0.00007592332,0.0001455005,0.00008681566,0.0005026588,0.000211908,0.00005705392,0.00002322134,0.0002469803,0.00001811096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001532734,"about_ca_system_score_gemma":0.0000331372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001041093,"about_ca_topic_score_gemma":8.029526e-7,"domain_scores_codex":[0.9984077,0.000119996,0.0006814706,0.0003891428,0.0001640112,0.0002377128],"domain_scores_gemma":[0.9992294,0.00004801639,0.0003086261,0.00027031,0.00006942394,0.00007423601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003848999,0.0002200744,0.8893217,0.00003012519,0.00008815661,0.00001448639,0.00486067,0.00005895023,0.0009164405,0.01669966,0.07327861,0.01412627],"study_design_scores_gemma":[0.009417245,0.001198816,0.896668,0.00007157599,0.00005351335,0.00003152319,0.01642667,0.000355498,0.004714486,0.008444367,0.06185257,0.0007656935],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7058218,0.0009641718,0.1592019,0.00276737,0.006389819,0.0005659751,0.00006835489,0.00007926254,0.1241414],"genre_scores_gemma":[0.9757451,0.00001810976,0.02196487,0.001864486,0.0001199153,0.00002291702,0.00004588793,0.00002152475,0.0001972126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2699233,"threshold_uncertainty_score":0.9992954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.038508249938427,"score_gpt":0.3220077002389653,"score_spread":0.2834994503005383,"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."}}