{"id":"W4403398073","doi":"10.23889/ijpds.v9i1.2372","title":"Research data use in a digital society: a deliberative public engagement","year":2024,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Deliberation; Public relations; Public engagement; Unintended consequences; Political science; Internet privacy; Business; Computer science; Politics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["research_integrity"],"domain":null,"study_design":"qualitative","genre":"empirical","about_ca_system":false,"about_ca_topic":true,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":["sts"],"domain":null,"study_design":"qualitative","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.2441443,0.001458049,0.00159875,0.005916906,0.06228736,0.03703761,0.006071597,0.01801782,0.005112997],"category_scores_gemma":[0.198599,0.00224804,0.002303318,0.004007942,0.1054698,0.0318306,0.08781565,0.03472155,0.001289468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02669396,"about_ca_system_score_gemma":0.05307173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006860584,"about_ca_topic_score_gemma":0.007422915,"domain_scores_codex":[0.5314417,0.4288076,0.005849218,0.007492239,0.01267585,0.0137333],"domain_scores_gemma":[0.5890028,0.3203559,0.01400461,0.02012647,0.01533892,0.0411713],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001109829,0.0002508394,0.003069169,0.0003567871,0.00006658483,0.001707997,0.9054459,0.0005165029,0.0006635602,0.05230208,0.01086429,0.02464524],"study_design_scores_gemma":[0.0000532855,0.0001410452,0.0009611925,0.0009101641,0.00002701127,0.0006052991,0.7331315,0.0006192345,0.0004070116,0.05958161,0.2034715,0.00009110959],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2304527,0.005589643,0.04855302,0.6399104,0.003404296,0.002201264,0.0001471349,0.0003152396,0.06942637],"genre_scores_gemma":[0.9217205,0.002916685,0.02231494,0.04203098,0.000819405,0.001641106,0.000102189,0.000232656,0.008221596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9939284,"threshold_uncertainty_score":0.9321046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9170635942163636,"score_gpt":0.7248893643938948,"score_spread":0.1921742298224688,"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."}}