{"id":"W2732643434","doi":"10.2139/ssrn.2995283","title":"Automation, Computerisation and Future Employment In Singapore","year":2016,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Digital Economy and Work Transformation","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Automation; Business; Labour economics; Demographic economics; Operations management; Economic growth; Engineering; Economics; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004298118,0.0001099705,0.0001940906,0.0006591141,0.001041969,0.00251997,0.0003059192,0.0005674108,0.008101083],"category_scores_gemma":[0.0008920378,0.00009632205,0.0002410528,0.002718749,0.0007750471,0.001437815,0.001871398,0.0008405229,0.0009459223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002680193,"about_ca_system_score_gemma":0.002526105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04563937,"about_ca_topic_score_gemma":0.1076885,"domain_scores_codex":[0.9995599,0.00007462122,0.00004879983,0.00004878643,0.00008016727,0.0001876467],"domain_scores_gemma":[0.9991444,0.0001228774,0.0003047992,0.00002681912,0.0001094448,0.0002916005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003051098,0.0002313473,0.8819067,0.000152231,0.00006224683,0.001871771,0.02510052,0.001082851,0.0006227512,0.03675994,0.003082941,0.04882153],"study_design_scores_gemma":[0.00001048936,0.0001817359,0.9456089,0.00006912932,0.00002005981,0.0002903168,0.03036339,0.001185871,0.0001791424,0.003063629,0.01900263,0.00002474262],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845945,0.000850526,0.00004858734,0.001838708,0.00001781941,0.000002942024,0.0002541806,0.000002913461,0.01238977],"genre_scores_gemma":[0.99657,0.000380212,0.00001276012,0.00003872175,0.000007207845,0.000002848295,0.00009210701,0.000001214475,0.002894856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04563937,"threshold_uncertainty_score":0.09074742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008728322349388012,"score_gpt":0.2558814951545683,"score_spread":0.2471531728051803,"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."}}