{"id":"W4398293932","doi":"10.7910/dvn/c2qo6j","title":"Replication Data for: How Responsive is Trade Adjustment Assistance?","year":2020,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Global trade and economics","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Replication (statistics); Computer science; Biology; Virology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002194831,0.001257326,0.0009008315,0.003008784,0.000710169,0.002780482,0.001957438,0.002008263,0.09062827],"category_scores_gemma":[0.01584255,0.0005660589,0.0009741677,0.005624582,0.0004998136,0.001698522,0.002058028,0.001777432,0.07741689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001789822,"about_ca_system_score_gemma":0.002572763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04805834,"about_ca_topic_score_gemma":0.05705813,"domain_scores_codex":[0.9983196,0.0002769045,0.0003073369,0.0003787233,0.0004225754,0.0002948977],"domain_scores_gemma":[0.9931258,0.001351773,0.001168652,0.001356749,0.002435233,0.0005617711],"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":[0.00009161777,0.00002446838,0.003329207,0.0003893843,0.00002659561,0.0000190067,0.00003137496,0.0001642013,0.00007994924,0.0005536655,0.9928169,0.002473735],"study_design_scores_gemma":[0.0005821935,0.00003931615,0.02481946,0.0003936903,0.00003506776,0.00005850786,0.0002675977,0.0004312703,0.0004106606,0.000967883,0.9719462,0.00004814001],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002964629,0.00004364646,0.0000417697,0.0002458564,0.00005013396,0.00001656364,0.9981291,0.0001029943,0.001073481],"genre_scores_gemma":[0.00179787,0.00006386355,0.0002391178,0.0001520788,0.00002709209,0.0001690706,0.994985,0.0000556467,0.002510265],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09062827,"threshold_uncertainty_score":0.3031818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1250026213887855,"score_gpt":0.2605512519887204,"score_spread":0.1355486305999349,"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."}}