{"id":"W4404997846","doi":"10.33424/futurum552","title":"How can artificial intelligence help to create a more inclusive labour market?","year":2024,"lang":"en","type":"article","venue":"","topic":"Digital Economy and Work Transformation","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Business; Artificial intelligence; Computer science; Labour economics; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000317768,0.00007743204,0.00008054332,0.00008770507,0.0002467012,0.0009342809,0.0001553022,0.00005235356,0.0006300418],"category_scores_gemma":[0.00007251816,0.00006992383,0.00004795981,0.0004552102,0.00008353289,0.000654972,0.00003171829,0.00006839409,0.0002049589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001092039,"about_ca_system_score_gemma":0.0001589144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001182257,"about_ca_topic_score_gemma":0.01029612,"domain_scores_codex":[0.9992791,0.00002999244,0.0001257326,0.0001661301,0.0001627009,0.0002363664],"domain_scores_gemma":[0.9996559,0.00007123662,0.00001202121,0.00007002155,0.00004877202,0.0001420708],"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":[0.000009021226,0.0000103712,0.00002185463,0.00001321964,0.000009310369,0.00000673464,0.0166483,0.00001340703,0.000005448825,0.6434421,0.001726293,0.3380939],"study_design_scores_gemma":[0.0000173248,0.0000554137,0.0001252245,0.00008640105,0.00001062137,0.000001288316,0.05750791,0.0006682294,0.001727764,0.1371988,0.8023168,0.0002842847],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04847438,0.00007276994,0.01560147,0.2063477,0.0006259268,0.0004448113,0.000048796,0.0003070495,0.7280771],"genre_scores_gemma":[0.9656297,0.00003287362,0.0002191845,0.0008250595,0.0002318407,0.00002108205,0.00000394805,0.000005950069,0.03303038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9171553,"threshold_uncertainty_score":0.9009292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01851337065376653,"score_gpt":0.2894460231822981,"score_spread":0.2709326525285315,"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."}}