{"id":"W3046250009","doi":"10.5840/symposium201923225","title":"The Division of Labour and Its Alien Effects","year":2019,"lang":"en","type":"article","venue":"Symposium","topic":"International Labor and Employment Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Alien; Division (mathematics); Sociology; Demography; Population; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.004312005,0.0005122662,0.0006436277,0.00150318,0.01016411,0.01038055,0.00114817,0.01020872,0.01223392],"category_scores_gemma":[0.006771217,0.0003768764,0.0008333237,0.001837763,0.040544,0.006604425,0.009014029,0.01025988,0.0007268646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005906434,"about_ca_system_score_gemma":0.004193121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01249225,"about_ca_topic_score_gemma":0.02051992,"domain_scores_codex":[0.9963883,0.001600845,0.00009783097,0.0003434297,0.0007338134,0.0008357746],"domain_scores_gemma":[0.9969541,0.002087115,0.0002220244,0.0002248243,0.0001972764,0.0003146851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003943972,0.000006718667,0.00005339632,0.000005267992,0.000001433591,0.00001542275,0.0008284093,0.00002238885,0.00001222077,0.9950047,0.003185881,0.0008601928],"study_design_scores_gemma":[0.00003315102,0.00001466629,0.001154115,0.0001432446,0.00001260702,0.00004914373,0.002425819,0.0001884803,0.00006245479,0.8724859,0.1234113,0.0000190737],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03222403,0.01874317,0.002472542,0.1730375,0.002211181,0.00002212923,0.00008789101,0.00002535293,0.7711762],"genre_scores_gemma":[0.8245412,0.00896028,0.00103054,0.04068687,0.005154864,0.000147665,0.00004085828,0.00006007286,0.1193776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01249225,"threshold_uncertainty_score":0.04285437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005470293016907634,"score_gpt":0.270301428323746,"score_spread":0.2648311353068384,"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."}}