{"id":"W4391229737","doi":"10.1080/13504851.2024.2305235","title":"Detecting corruption from outer space","year":2024,"lang":"en","type":"article","venue":"Applied Economics Letters","topic":"Impact of Light on Environment and Health","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Space (punctuation); Language change; Outer space; Econometrics; Economics; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.006073822,0.0003231711,0.0004728663,0.00248411,0.0006807084,0.002168528,0.0005265988,0.0005856593,0.001587701],"category_scores_gemma":[0.04190913,0.0002185644,0.000437997,0.00263292,0.001404527,0.002204887,0.002560641,0.00088482,0.0003969769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008609392,"about_ca_system_score_gemma":0.001030079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006945486,"about_ca_topic_score_gemma":0.006059784,"domain_scores_codex":[0.9939954,0.003353952,0.0004145608,0.0007490336,0.0007919015,0.0006951362],"domain_scores_gemma":[0.9486686,0.01882313,0.01778854,0.00821615,0.005281741,0.001221799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008537762,0.00004672587,0.9628707,0.00004745056,0.00006654779,0.0001089117,0.001263678,0.002735011,0.00038046,0.003878715,0.001139383,0.02737723],"study_design_scores_gemma":[0.00001714516,0.0001438087,0.8950332,0.0001669726,0.0001223192,0.000209437,0.005320793,0.06382723,0.003466231,0.02194902,0.009691155,0.00005272618],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9753336,0.0002064922,0.0172484,0.0006862226,0.00003705162,0.00003586854,0.000742841,0.00006405928,0.005645596],"genre_scores_gemma":[0.9979045,0.00002965225,0.001359513,0.00004145638,0.00002059441,0.00001429513,0.0003103882,0.000006713392,0.000312808],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006945486,"threshold_uncertainty_score":0.03212178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01021898051442143,"score_gpt":0.2031825227655416,"score_spread":0.1929635422511202,"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."}}