{"id":"W2044078488","doi":"10.1108/mbe-11-2014-0041","title":"Measuring social issues in sustainable supply chains","year":2015,"lang":"en","type":"article","venue":"Measuring Business Excellence","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Triple bottom line; Supply chain; Originality; Computer science; Context (archaeology); Measure (data warehouse); Content analysis; Quantitative analysis (chemistry); Systematic review; Supply chain management; Sustainability; Environmental economics; Qualitative research; Marketing; Data mining; Business; Sociology; Economics; Social science","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":[0.04291718,0.001016802,0.0009370016,0.02733111,0.002877393,0.008383791,0.001224951,0.001289573,0.002978126],"category_scores_gemma":[0.09748708,0.0003643079,0.0009459727,0.02833499,0.005476885,0.01171946,0.005853464,0.001250236,0.0003510652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007782168,"about_ca_system_score_gemma":0.009251919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001958784,"about_ca_topic_score_gemma":0.003517676,"domain_scores_codex":[0.9285726,0.03972617,0.007040153,0.001648129,0.02192556,0.001087304],"domain_scores_gemma":[0.8100177,0.1075117,0.03784946,0.004059026,0.03819265,0.002369466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002241344,0.0003268516,0.1587167,0.02118352,0.001262967,0.0006033989,0.0364185,0.007357187,0.002811687,0.1227458,0.008704006,0.6396451],"study_design_scores_gemma":[0.00005224467,0.00172617,0.2475231,0.03557912,0.001284937,0.001514608,0.1961642,0.01100074,0.01041059,0.2271583,0.2670261,0.0005597967],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.608763,0.08100216,0.1147406,0.02543718,0.00157796,0.002510895,0.00186921,0.0002409764,0.1638581],"genre_scores_gemma":[0.9552427,0.01237502,0.02918977,0.0006426716,0.0002919869,0.0004938437,0.0003518727,0.00003188717,0.00138028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04291718,"threshold_uncertainty_score":0.2269704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05230071178198305,"score_gpt":0.2337008200938311,"score_spread":0.1814001083118481,"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."}}