{"id":"W4405463915","doi":"10.2139/ssrn.5022896","title":"Engineering data equity: the LISTEN principles","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Equity (law); Computer science; Business; Economics; Data science; Political science; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.03413237,0.0009589082,0.002114707,0.003704232,0.003989628,0.01119561,0.003252685,0.009034164,0.02517699],"category_scores_gemma":[0.1527341,0.001137178,0.00175626,0.003471245,0.02336291,0.03399502,0.01030098,0.01129533,0.003257528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003363157,"about_ca_system_score_gemma":0.004395609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00215179,"about_ca_topic_score_gemma":0.001243673,"domain_scores_codex":[0.9772794,0.01041888,0.001109254,0.003169619,0.006624229,0.001398581],"domain_scores_gemma":[0.8869647,0.08799886,0.002712627,0.01461623,0.00619502,0.001512586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001326797,0.00001604274,0.0001855135,0.00003134651,0.00001077184,0.00001562366,0.0001350886,0.0002652826,0.00003646027,0.988452,0.00336582,0.007472627],"study_design_scores_gemma":[0.00001211832,0.000007014936,0.00005366715,0.0000311567,0.000006902275,0.00002427306,0.00004286887,0.0007890099,0.0001054625,0.9935217,0.005400797,0.000004969925],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0126069,0.002662139,0.6908734,0.120442,0.001578884,0.0002317361,0.0004275192,0.0003291493,0.1708484],"genre_scores_gemma":[0.7235315,0.003497268,0.1580377,0.0341633,0.008011009,0.001432,0.0003155927,0.001005388,0.07000618],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9967473,"threshold_uncertainty_score":0.1805114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3866499839417282,"score_gpt":0.4951689738661905,"score_spread":0.1085189899244624,"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."}}