{"id":"W2941522337","doi":"10.1007/s10664-019-09711-y","title":"Characterizing industry-academia collaborations in software engineering: evidence from 101 projects","year":2019,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Software Engineering Techniques and Practices","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"ITEA3; Estonian Research Competency Council; University of Calgary; Tartu Ülikool; Eesti Teadusagentuur; Tekes; Universidade do Minho; Norges Forskningsråd; ITEA; Strategic Research Council; Fundação para a Ciência e a Tecnologia; Åbo Akademi; Universidade Federal de Santa Catarina","keywords":"Context (archaeology); Relevance (law); Engineering; Empirical research; Engineering management; Business; Knowledge management; Marketing; Computer science; Political science; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0005556786,0.0005238479,0.0005455241,0.0004537038,0.00007038322,0.0003659015,0.001357972,0.0009862276,0.0001049691],"category_scores_gemma":[0.006927787,0.0005659107,0.0001179272,0.002133546,0.00001556728,0.002377534,0.0005272228,0.002611461,0.0001245586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003688305,"about_ca_system_score_gemma":0.0002599866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001085631,"about_ca_topic_score_gemma":0.000004189284,"domain_scores_codex":[0.996932,0.00006171865,0.0006472353,0.0009506457,0.0005834638,0.0008248835],"domain_scores_gemma":[0.9958127,0.002566217,0.0001628735,0.0009811198,0.000117554,0.0003596042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003067973,0.0001486716,0.881735,0.0003508144,0.0001041696,0.0002009462,0.002949352,0.1020636,0.006509173,0.0004335084,0.001920123,0.003554035],"study_design_scores_gemma":[0.001015726,0.0003377745,0.6988345,0.002671861,0.0000394456,0.00006973884,0.00004996161,0.2042892,0.008203299,0.000109496,0.0816521,0.002726858],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.474116,0.0005796123,0.5202582,0.0007226068,0.0009017498,0.0005069232,0.00001560456,0.002891776,0.000007475404],"genre_scores_gemma":[0.5362819,0.00006351092,0.4625292,0.0004523119,0.0002873437,0.0001898865,0.00002153414,0.00009357416,0.00008079327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1829004,"threshold_uncertainty_score":0.9996896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04699284876742493,"score_gpt":0.2945148710774645,"score_spread":0.2475220223100396,"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."}}