{"id":"W2032219610","doi":"10.1109/ms.2009.16","title":"Mining Task-Based Social Networks to Explore Collaboration in Software Teams","year":2008,"lang":"en","type":"article","venue":"IEEE Software","topic":"Software Engineering Research","field":"Computer Science","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Task (project management); IBM; Social network (sociolinguistics); Context (archaeology); World Wide Web; Software; Software development; Data science; Social network analysis; Software engineering; Social software engineering; Knowledge management; Software construction; Social media; Engineering; Systems 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003623129,0.0002838446,0.0003163596,0.0004572268,0.0005099432,0.000127872,0.001085371,0.0002097113,0.0000116895],"category_scores_gemma":[0.001425332,0.0003203521,0.0000825458,0.002626866,0.00006397982,0.0004981292,0.0002407164,0.0003722968,0.00006866832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003875041,"about_ca_system_score_gemma":0.0004891845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004044393,"about_ca_topic_score_gemma":0.00004918139,"domain_scores_codex":[0.9973141,0.0001074335,0.0003682284,0.0006960005,0.0007339552,0.0007802775],"domain_scores_gemma":[0.9979517,0.0008489945,0.0000712139,0.0006111813,0.0002789373,0.0002379849],"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":[0.00008529823,0.0002907296,0.3547575,0.000106641,0.00003831733,0.0008502091,0.02721697,0.5143332,0.0002851356,0.00006547888,0.05618405,0.04578649],"study_design_scores_gemma":[0.01056819,0.001903429,0.6125845,0.001191394,0.00003838621,0.0002144255,0.001596717,0.3158685,0.009746885,0.0006573095,0.0386646,0.006965598],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3617693,0.00007137973,0.635996,0.0003658946,0.000628322,0.0002872776,0.000004644364,0.0008724713,0.000004659837],"genre_scores_gemma":[0.8428433,0.000004058705,0.1559495,0.0005050569,0.0003295285,0.0002053891,0.00001754047,0.00005193388,0.00009369454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.481074,"threshold_uncertainty_score":0.9999248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03378415132012308,"score_gpt":0.2903802627805691,"score_spread":0.256596111460446,"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."}}