{"id":"W2945530585","doi":"10.1007/s10664-019-09719-4","title":"Fostering good coding practices through individual feedback and gamification: an industrial case study","year":2019,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Coding (social sciences); Variety (cybernetics); Computer science; Best practice; Code review; Software; Quality (philosophy); Software engineering; Knowledge management; Software quality; Data science; Software development; Management","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01005052,0.0007212798,0.0003737414,0.001391348,0.002142829,0.001930712,0.001971421,0.001879287,0.001723377],"category_scores_gemma":[0.03139336,0.0004039415,0.0003262348,0.0009321265,0.002145197,0.001470084,0.002692571,0.001596018,0.0003639075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00142287,"about_ca_system_score_gemma":0.002366584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002017097,"about_ca_topic_score_gemma":0.004475044,"domain_scores_codex":[0.9950151,0.003146564,0.0001589203,0.0003674304,0.0007701374,0.0005417849],"domain_scores_gemma":[0.9541145,0.03347465,0.002359093,0.004699768,0.002958063,0.002393989],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003174185,0.0573215,0.1336841,0.0009033341,0.0001603614,0.006362186,0.1065107,0.03226627,0.02285739,0.01645461,0.004168423,0.616137],"study_design_scores_gemma":[0.003261048,0.05270786,0.1753225,0.001693288,0.0006325358,0.01052491,0.1898978,0.3676045,0.1071524,0.04738656,0.04292326,0.00089339],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905956,0.00003796426,0.00637731,0.00021076,0.000007289666,0.000182205,0.00001360846,0.00005943716,0.002515754],"genre_scores_gemma":[0.9833059,0.00005176396,0.01532198,0.00003996227,0.000003633964,0.00009960833,0.00002128288,0.00001628678,0.001139661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9899495,"threshold_uncertainty_score":0.05315286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1720305715459125,"score_gpt":0.3707082639811677,"score_spread":0.1986776924352552,"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."}}