{"id":"W7095548914","doi":"","title":"Factor mobility, efficiency and language discrimination: analysis of four company scenarios in Catalonia","year":2015,"lang":"en","type":"article","venue":"","topic":"Colonialism, slavery, and trade","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Factor (programming language); Diversity (politics); Distribution (mathematics); Linguistic diversity; On Language; Work (physics)","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":[0.002832197,0.0007654517,0.000690148,0.002308409,0.001410841,0.002621382,0.001109653,0.001125638,0.002006927],"category_scores_gemma":[0.004323138,0.0003147871,0.0009617602,0.0033997,0.001346083,0.001062291,0.001603053,0.000519104,0.0002900712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006987157,"about_ca_system_score_gemma":0.001626777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2251766,"about_ca_topic_score_gemma":0.1808108,"domain_scores_codex":[0.9979137,0.001307442,0.00009091323,0.0001765883,0.0001533524,0.0003581262],"domain_scores_gemma":[0.9959847,0.00278563,0.0002403063,0.0001890421,0.0005872617,0.0002131604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002408247,0.002182356,0.7825704,0.0008254379,0.0006154376,0.01247463,0.03943239,0.08813379,0.002604509,0.01459651,0.002641461,0.05151488],"study_design_scores_gemma":[0.00027952,0.0008712857,0.6928861,0.0003312716,0.0002400553,0.0008210231,0.2014017,0.08971649,0.001262188,0.002980805,0.009066192,0.0001433408],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982616,0.00008979869,0.0002983588,0.00005387251,0.000001876346,0.00003658193,0.0001182148,0.000003199421,0.001136394],"genre_scores_gemma":[0.998013,0.0001144748,0.0007785994,0.00001415868,0.000001715326,0.00003494472,0.0004427728,0.00000395708,0.0005964257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2251766,"threshold_uncertainty_score":0.4477319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05694966054548522,"score_gpt":0.3435219023788619,"score_spread":0.2865722418333767,"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."}}