{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003950306,0.0002543488,0.0003056556,0.0003843019,0.0002319565,0.00009379684,0.0003051778,0.0001727126,0.0008069437],"category_scores_gemma":[0.00006980913,0.0002489098,0.0001914039,0.0006016027,0.0001203608,0.0001511129,0.0000257242,0.00009756087,0.0003657047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001758351,"about_ca_system_score_gemma":0.0000838055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002605822,"about_ca_topic_score_gemma":0.0007780007,"domain_scores_codex":[0.9978684,0.00009874086,0.0006548137,0.0007096535,0.0002511521,0.0004173091],"domain_scores_gemma":[0.9979196,0.0001648958,0.0004387075,0.0007592693,0.0005741105,0.0001434451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003308995,0.001234874,0.7603714,0.00009663476,0.001845871,0.000004136986,0.03569562,0.00001908114,0.00292026,0.01124177,0.006960653,0.1792787],"study_design_scores_gemma":[0.0004086466,0.0001524421,0.9314011,0.00001897948,0.0001871074,0.000006729157,0.004291228,0.0001963035,0.001176481,0.00006470976,0.06180224,0.0002940251],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9668417,0.0001646405,0.02898377,0.0006563697,0.0008084201,0.001374921,0.00006097721,0.0001487645,0.0009604645],"genre_scores_gemma":[0.9837946,0.00001706394,0.001659602,0.0002467535,0.0004830084,0.002933129,0.0009219701,0.00004116501,0.009902663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1789847,"threshold_uncertainty_score":0.9999963,"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."}}