{"id":"W2897401553","doi":"10.2196/11336","title":"Patterns Among 754 Gamification Cases: Content Analysis for Gamification Development","year":2018,"lang":"en","type":"article","venue":"JMIR Serious Games","topic":"Educational Games and Gamification","field":"Psychology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Kangwon National University; National Research Foundation","keywords":"Content (measure theory); Content analysis; Computer science; Psychology; Sociology; Mathematics; Social science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006294711,0.0006325783,0.0007076865,0.01455355,0.001210624,0.001874399,0.001176098,0.0006270171,0.003177823],"category_scores_gemma":[0.04067387,0.0004236438,0.0009964894,0.007442911,0.001337546,0.001647946,0.002961188,0.0007120706,0.0006824284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003326716,"about_ca_system_score_gemma":0.001856717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003130504,"about_ca_topic_score_gemma":0.00509293,"domain_scores_codex":[0.9931105,0.002375572,0.001023495,0.0008675583,0.002067775,0.0005551156],"domain_scores_gemma":[0.967481,0.02253236,0.00247289,0.00137566,0.00556194,0.0005761881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009151084,0.001282867,0.4504552,0.002438508,0.0001768563,0.002636206,0.1806554,0.001881366,0.007935923,0.006009815,0.007328687,0.3382841],"study_design_scores_gemma":[0.00009362355,0.0007471208,0.7509691,0.001730102,0.0002634248,0.002078962,0.1804794,0.02476694,0.0085907,0.005563916,0.02448328,0.0002335464],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831831,0.0001459686,0.007897201,0.000127685,0.00001751419,0.002713261,0.001275941,0.000144289,0.004495081],"genre_scores_gemma":[0.9449083,0.0004060344,0.04151803,0.00005572693,0.00001484837,0.007463976,0.00309243,0.00007781013,0.002462821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01455355,"threshold_uncertainty_score":0.03329003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07387656179198666,"score_gpt":0.3579462725046666,"score_spread":0.2840697107126799,"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."}}