{"id":"W2946747703","doi":"10.1145/3305160.3305161","title":"A Productivity Framework for Software Development Literature Review","year":2019,"lang":"en","type":"article","venue":"","topic":"Software Engineering Techniques and Practices","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Productivity; Assertion; Computer science; Context (archaeology); Measure (data warehouse); Software; Software development; Work (physics); Productivity model; Engineering; Data mining; Total factor productivity; Economics","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":[0.0151052,0.002124772,0.002593348,0.06899849,0.002886415,0.01050215,0.003274756,0.003380471,0.0105928],"category_scores_gemma":[0.03255637,0.001059994,0.002829673,0.05815994,0.00312298,0.01405643,0.00481546,0.002880027,0.003197924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009997351,"about_ca_system_score_gemma":0.01528787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008654003,"about_ca_topic_score_gemma":0.007722088,"domain_scores_codex":[0.9908082,0.004097551,0.001648552,0.0007766738,0.002199201,0.000469892],"domain_scores_gemma":[0.9698943,0.02109302,0.002121104,0.0006114733,0.005690664,0.0005893933],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002723485,0.00008020578,0.0009279766,0.02454937,0.0002224637,0.0003927678,0.001271519,0.004325263,0.0003174258,0.6408678,0.0588458,0.2681721],"study_design_scores_gemma":[0.00002178358,0.00005636668,0.001926594,0.0408546,0.0001995293,0.0005000329,0.002224506,0.002528159,0.0001865634,0.3546023,0.5968216,0.00007808376],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"methods","genre_scores_codex":[0.001386277,0.8278444,0.1043254,0.0181204,0.001910047,0.0007462061,0.002494663,0.0002767036,0.04289593],"genre_scores_gemma":[0.03318939,0.8240318,0.1204212,0.004001047,0.002738091,0.002966073,0.003455768,0.0001077558,0.009088919],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9848948,"threshold_uncertainty_score":0.07988489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01444983839340858,"score_gpt":0.2800131099364815,"score_spread":0.265563271543073,"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."}}