{"id":"W2890039290","doi":"10.23889/ijpds.v3i4.647","title":"Power of Linked Administrative Data","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data Analysis and Archiving","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Subsidy; Service (business); Intervention (counseling); Political science; Business; Medicine; Nursing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.003639573,0.00005600403,0.0000933477,0.0002175118,0.0007888042,0.0004098742,0.005547054,0.00002060726,0.0002898152],"category_scores_gemma":[0.003613315,0.00004900317,0.00002871436,0.0003418059,0.00075642,0.004467048,0.0007874961,0.00007489231,0.000009562428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005312133,"about_ca_system_score_gemma":0.0004726477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000481766,"about_ca_topic_score_gemma":0.001209484,"domain_scores_codex":[0.9979454,0.00005449965,0.0003612947,0.0003224419,0.001131681,0.0001847101],"domain_scores_gemma":[0.9978805,0.0001431375,0.0003764617,0.0006416956,0.0008437934,0.0001144123],"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":[0.0003425778,0.000598076,0.1590339,0.00001022642,0.0004581369,0.00001651683,0.01723931,0.00006269756,0.00809134,0.5284763,0.06153317,0.2241378],"study_design_scores_gemma":[0.0007583961,0.0002236103,0.1874815,0.0001631259,0.0000663161,0.00002767115,0.005560596,0.03968542,0.0002969585,0.01867092,0.7466542,0.0004112596],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7555575,0.0001035499,0.1577998,0.01593399,0.01758946,0.000831179,0.01262236,0.00009040325,0.03947176],"genre_scores_gemma":[0.9866961,0.00002991737,0.01143633,0.00009717133,0.0008428465,6.183143e-7,0.0006618163,0.000003441767,0.0002317275],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.685121,"threshold_uncertainty_score":0.9998334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2130589972291754,"score_gpt":0.5135269404990472,"score_spread":0.3004679432698718,"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."}}