{"id":"W2096287423","doi":"10.1109/icse.2009.5070504","title":"How tagging helps bridge the gap between social and technical aspects in software development","year":2009,"lang":"en","type":"article","venue":"","topic":"Software Engineering Techniques and Practices","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Bridge (graph theory); Computer science; Mechanism (biology); Empirical research; Knowledge management; Software; Software development; Domain (mathematical analysis); Collaborative software; Work (physics); Software engineering; Data science; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.03508907,0.0007305266,0.00062282,0.004099123,0.005291102,0.008610227,0.001630661,0.002553592,0.001972124],"category_scores_gemma":[0.08970578,0.001021537,0.0006363461,0.002956833,0.007001232,0.01972554,0.01017706,0.002104492,0.000711923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001909217,"about_ca_system_score_gemma":0.004581258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002428844,"about_ca_topic_score_gemma":0.00329918,"domain_scores_codex":[0.972509,0.01924821,0.001974794,0.002152096,0.003286564,0.0008293726],"domain_scores_gemma":[0.8101066,0.1394767,0.01437741,0.02860134,0.004501154,0.002936772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004273769,0.0007861323,0.103882,0.0008199092,0.0001623638,0.00155389,0.1597168,0.004694945,0.02971874,0.09446211,0.002482122,0.6012935],"study_design_scores_gemma":[0.0002924279,0.001731509,0.1002225,0.001597968,0.000643178,0.006080466,0.0953327,0.07265597,0.04471047,0.517341,0.1584343,0.0009575139],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5363617,0.0005406779,0.4414756,0.006385932,0.0001620335,0.0002089527,0.00004560828,0.001066631,0.01375282],"genre_scores_gemma":[0.856277,0.0003006278,0.1403808,0.0004244033,0.00006895447,0.0001314971,0.00006694479,0.0002239597,0.00212574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03508907,"threshold_uncertainty_score":0.185571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04748437766172536,"score_gpt":0.2850500230728536,"score_spread":0.2375656454111282,"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."}}