{"id":"W2788622724","doi":"","title":"Policy lensing of futures intelligence: connecting foresight with decision making contexts","year":2017,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Complex Systems and Decision Making","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute on Governance","funders":"","keywords":"Futures contract; Futures studies; Computer science; Data science; Economics; Artificial intelligence; Financial economics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.02040155,0.000571833,0.001245938,0.001183539,0.001466097,0.002624122,0.004540924,0.0004287344,0.0002104248],"category_scores_gemma":[0.03937104,0.0004442356,0.0004984161,0.0008430254,0.0004941481,0.0003929205,0.004069804,0.0008622224,0.00004434026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001387419,"about_ca_system_score_gemma":0.0009161767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001580755,"about_ca_topic_score_gemma":0.007560095,"domain_scores_codex":[0.9882262,0.004758746,0.002126782,0.001639615,0.00267796,0.0005707414],"domain_scores_gemma":[0.9629001,0.01810135,0.004151075,0.00618034,0.008426216,0.0002408858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001134571,0.0001535979,0.003998314,0.00007996357,0.00007882288,0.0000166527,0.01090834,0.001132273,0.0003927955,0.2345422,0.0008858173,0.7476977],"study_design_scores_gemma":[0.000892268,0.000003766703,0.01909317,0.02209997,0.0000785864,0.0001485972,0.002680327,0.04234434,0.01382599,0.8903994,0.007145843,0.001287766],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1659794,0.002149623,0.7767111,0.001873442,0.0006280072,0.00065951,0.00006027714,0.0001322767,0.05180636],"genre_scores_gemma":[0.9002106,0.00004954584,0.09713836,0.00005553322,0.0001055825,0.00001558942,0.00001496024,0.00006083303,0.002348985],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.74641,"threshold_uncertainty_score":0.9998339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06975180311565167,"score_gpt":0.3628905809988887,"score_spread":0.293138777883237,"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."}}