{"id":"W2765147456","doi":"10.4018/ijgcms.2017070103","title":"The Design of Disciplinarily-Integrated Games as Multirepresentational Systems","year":2017,"lang":"en","type":"article","venue":"International Journal of Gaming and Computer-Mediated Simulations","topic":"Educational Games and Gamification","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Science Foundation","keywords":"Affordance; Embodied cognition; Computer science; Leverage (statistics); Bridging (networking); Generalizability theory; Cognitive science; Human–computer interaction; Artificial intelligence; Psychology","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":[0.0004878842,0.0001162683,0.0001762654,0.0001649068,0.0003738934,0.0002929532,0.0006221847,0.00006936097,0.00003853732],"category_scores_gemma":[0.0004255812,0.00008406873,0.0000786797,0.00005938324,0.0002276792,0.0002330208,0.00007090711,0.0001766301,0.00001146456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004435363,"about_ca_system_score_gemma":0.000130673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001766648,"about_ca_topic_score_gemma":0.000007045373,"domain_scores_codex":[0.998472,0.0001586606,0.000651032,0.0001518347,0.0004399219,0.0001264924],"domain_scores_gemma":[0.995544,0.001749091,0.001155459,0.0002629482,0.001210168,0.00007829529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001840302,0.001982776,0.1870912,0.00003921718,0.004637283,0.0001224321,0.02340908,0.3711105,0.003159194,0.1773889,0.009733512,0.2194856],"study_design_scores_gemma":[0.002202317,0.0003209948,0.6122302,0.000252014,0.00009031775,0.0002616647,0.00321585,0.3675433,0.00006985919,0.003159726,0.01041999,0.0002337589],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7910056,0.001152442,0.1949991,0.004066515,0.007855676,0.0003020186,0.00008532049,0.00002111557,0.0005122508],"genre_scores_gemma":[0.9970657,0.0001009464,0.001480371,0.00003339388,0.0006539102,0.000007335179,0.00003661969,0.0000112911,0.0006104906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.425139,"threshold_uncertainty_score":0.3428223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04261235822845437,"score_gpt":0.3787819807414731,"score_spread":0.3361696225130187,"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."}}