{"id":"W3133564273","doi":"10.1145/3479529","title":"\"@alex, this fixes #9\": Analysis of Referencing Patterns in Pull Request Discussions","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Software Engineering Research","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Referent; Variety (cybernetics); Source code; Thread (computing); Common ground; World Wide Web; User interface; Software; Interface (matter); Information retrieval; Human–computer interaction; Programming language; Artificial intelligence; Psychology","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":[0.004095323,0.0003713971,0.0002878324,0.005458208,0.001439264,0.001857628,0.0009368566,0.001038702,0.003413505],"category_scores_gemma":[0.02490394,0.0002660592,0.0004090612,0.004130905,0.000793255,0.003012015,0.002161182,0.0007919684,0.001690443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009878027,"about_ca_system_score_gemma":0.001048493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007060034,"about_ca_topic_score_gemma":0.01234268,"domain_scores_codex":[0.9956414,0.001527627,0.000451783,0.0007918999,0.001232924,0.0003544268],"domain_scores_gemma":[0.9586455,0.02828632,0.004852649,0.0029153,0.004238757,0.001061528],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001780741,0.0007497211,0.4527866,0.002953312,0.0001878295,0.002996071,0.1332086,0.001886151,0.03051092,0.01409253,0.07538552,0.2834621],"study_design_scores_gemma":[0.00009223655,0.0004377319,0.6344867,0.0008416133,0.0001346848,0.001793212,0.08497528,0.02155193,0.02566457,0.007126578,0.2226252,0.0002703326],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9548907,0.0004465431,0.01622886,0.0008191107,0.00008844796,0.0003572084,0.01559155,0.003095537,0.008481985],"genre_scores_gemma":[0.8965058,0.0004112915,0.04211441,0.0006309682,0.00008061048,0.0009029069,0.04279707,0.001264021,0.01529292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9959047,"threshold_uncertainty_score":0.02165842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05804857565293148,"score_gpt":0.3381942710071979,"score_spread":0.2801456953542664,"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."}}