{"id":"W2756273103","doi":"10.1145/3123766","title":"Incentives and Gamification","year":2017,"lang":"en","type":"article","venue":"XRDS Crossroads The ACM Magazine for Students","topic":"Educational Games and Gamification","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Israel Academy of Sciences and Humanities","keywords":"Hebrew; Citation; Incentive; Library science; Computer science; History; Classics; Economics","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":[0.000531338,0.0001296215,0.000124047,0.00003267182,0.0009321733,0.0004826316,0.001966158,0.00006388724,0.00009441132],"category_scores_gemma":[0.000620605,0.00009583942,0.00005659344,0.0000370072,0.000318052,0.0001593911,0.0004679035,0.00009076366,0.0002289399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002416189,"about_ca_system_score_gemma":0.00001568734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004156239,"about_ca_topic_score_gemma":0.00002230045,"domain_scores_codex":[0.9989821,0.00003718842,0.0002079651,0.0003336863,0.0002060041,0.000233039],"domain_scores_gemma":[0.9973179,0.0001378829,0.000263749,0.002094947,0.0001331018,0.00005243714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002426487,0.0006551007,0.8141136,0.00003264028,0.0002252441,8.801954e-7,0.009996061,8.711963e-7,0.002157748,0.06060466,0.04778331,0.06418725],"study_design_scores_gemma":[0.0007159349,0.00005681451,0.8929996,0.0000090762,0.00003202336,0.00000339843,0.0004772824,0.000009422379,0.00005887523,0.007915905,0.09761894,0.0001026636],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9823757,0.0004181336,0.0003981816,0.00954053,0.0011589,0.0006886209,0.00004329064,0.00003290281,0.005343687],"genre_scores_gemma":[0.9658511,0.0000630029,0.0006303461,0.0002416338,0.0003052975,0.0003234425,0.00003981646,0.00002044407,0.03252487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07888608,"threshold_uncertainty_score":0.7169618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05180303710280908,"score_gpt":0.4481360201131945,"score_spread":0.3963329830103854,"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."}}