{"id":"W4398834468","doi":"10.7910/dvn/sczu92","title":"PROSPERED Dataset: Maternity leave policy","year":2018,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research","keywords":"Maternity leave; Business; Data science; Computer science; Demographic economics; Economics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001363729,0.0004550335,0.0004943213,0.0001911068,0.0008622576,0.0004664245,0.0019336,0.0004676119,0.06223281],"category_scores_gemma":[0.001029329,0.0004428182,0.0001426713,0.0003147924,0.001353532,0.0005451322,0.0009310549,0.0003629873,0.166269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006980165,"about_ca_system_score_gemma":0.0009114531,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.09994319,"about_ca_topic_score_gemma":0.02910795,"domain_scores_codex":[0.9959698,0.0004751777,0.0005242988,0.0009284489,0.001169658,0.0009325729],"domain_scores_gemma":[0.9966416,0.00007225671,0.0003193192,0.002371313,0.0001316568,0.0004638987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004483461,0.0002543979,0.000233717,0.000178147,0.00007123643,0.00002134844,0.0003904779,1.190686e-7,0.000003164145,0.0003066367,0.9983807,0.0001152855],"study_design_scores_gemma":[0.0003327669,0.00007263172,0.0001562892,0.00007265202,0.0001123676,0.000001438677,0.0004267549,7.106694e-7,0.00001443163,0.0002466871,0.9980775,0.0004858157],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001506167,8.584229e-7,0.000001935338,0.00009190776,0.001260129,0.0009488185,0.995672,0.00009226749,0.001781443],"genre_scores_gemma":[0.00006608533,0.0007228101,0.00005795867,0.0008024097,0.003033464,0.00007352343,0.993992,0.00002853526,0.00122323],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1040362,"threshold_uncertainty_score":0.9998024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1474095616792713,"score_gpt":0.4129797498793699,"score_spread":0.2655701882000986,"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."}}