{"id":"W2573959285","doi":"10.1287/mnsc.2016.2619","title":"Industrial Development Through Tacit Knowledge Seeding: Evidence from the Bangladesh Garment Industry","year":2017,"lang":"en","type":"article","venue":"Management Science","topic":"Firm Innovation and Growth","field":"Economics, Econometrics and Finance","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Tacit knowledge; Industrialisation; Business; Industrial organization; Empirical evidence; Developing country; Marketing; Knowledge management; Economics; Economic growth; Market economy; Computer 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":[0.001066668,0.0001996792,0.0002163435,0.001681933,0.001177608,0.001518915,0.0004463386,0.0006813246,0.00786983],"category_scores_gemma":[0.004318313,0.0001814058,0.0001669661,0.002382156,0.001649156,0.001331217,0.001555147,0.0006519507,0.001067979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001238095,"about_ca_system_score_gemma":0.0008557274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02033443,"about_ca_topic_score_gemma":0.04114159,"domain_scores_codex":[0.9995903,0.000106561,0.00002751467,0.00006621989,0.00009318803,0.0001161576],"domain_scores_gemma":[0.9906077,0.004725016,0.002738385,0.0005071947,0.0008243258,0.0005972669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006112837,0.0007134853,0.8600144,0.0006327251,0.0001132033,0.004210377,0.02608745,0.001587168,0.002784071,0.00988922,0.00222443,0.09113213],"study_design_scores_gemma":[0.0000475273,0.0003971152,0.9422362,0.0003031827,0.00006869303,0.0005426239,0.03776623,0.0009462174,0.001798198,0.002952622,0.01289168,0.00004968456],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9803298,0.00063522,0.0002825893,0.0004032532,0.000002860472,0.00002118743,0.0001976439,0.000004783281,0.01812281],"genre_scores_gemma":[0.9972883,0.0009345801,0.0001257774,0.00003946012,0.000004120054,0.000006446426,0.0001164768,0.000001466569,0.001483208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02033443,"threshold_uncertainty_score":0.04043216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2213470362130286,"score_gpt":0.3062235318449383,"score_spread":0.08487649563190966,"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."}}