{"id":"W4406991643","doi":"10.1017/elo.2024.49","title":"Taxing data when the United States disagrees","year":2024,"lang":"en","type":"article","venue":"European Law Open","topic":"Gender, Labor, and Family Dynamics","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada; Università degli Studi di Trento","keywords":"Political science","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00224411,0.0001009312,0.00008757967,0.00002556736,0.0008389292,0.001962468,0.003194927,0.00001929263,0.0001960072],"category_scores_gemma":[0.00008667068,0.00006714595,0.00002390583,0.0003375935,0.000336416,0.0005828009,0.001493785,0.0001674996,0.0005265414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002868627,"about_ca_system_score_gemma":0.00007363498,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01883419,"about_ca_topic_score_gemma":0.01048871,"domain_scores_codex":[0.9982531,0.0007413185,0.0001547394,0.000325378,0.0002496856,0.000275786],"domain_scores_gemma":[0.9989602,0.0001947559,0.00003187912,0.0006903139,0.00003998894,0.00008287395],"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":[0.000006140804,0.00003234978,0.001507812,0.00001751911,0.00008230509,0.0000965079,0.08992816,0.0001180791,0.0000157898,0.7717373,0.1317714,0.004686534],"study_design_scores_gemma":[0.0000617804,0.000009681433,0.00180255,0.00003681786,0.00001919983,7.372598e-7,0.01763731,0.001838519,4.924191e-7,0.003456751,0.9750168,0.0001194177],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04295315,0.0006051256,0.000719133,0.007801023,0.001042243,0.0004908827,0.0002686211,0.0002421868,0.9458776],"genre_scores_gemma":[0.9649559,0.0006430101,0.0007032861,0.00399844,0.0007236609,0.000004165404,0.0006994908,0.00006247743,0.02820956],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9220027,"threshold_uncertainty_score":0.9990736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09540188759340276,"score_gpt":0.3398227007786668,"score_spread":0.244420813185264,"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."}}