{"id":"W3031470900","doi":"10.1080/10304312.2020.1764781","title":"Expectation and anticipation: research assemblages for elections","year":2020,"lang":"en","type":"article","venue":"Continuum","topic":"Social and Cultural Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Anticipation (artificial intelligence); Scholarship; Sociology; Politics; Ontology; Citizen journalism; Public relations; Media studies; Epistemology; Political science; Computer science; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002391884,0.00002863288,0.00005278031,0.00001411877,0.00073537,0.00008456013,0.00005168362,0.00004410824,0.00001699589],"category_scores_gemma":[0.0007523898,0.00002740402,0.00002070912,0.0002353698,0.0001020935,0.0001149517,0.00001081904,0.00005655886,0.000008467122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001768602,"about_ca_system_score_gemma":0.00003868063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001683826,"about_ca_topic_score_gemma":0.00218775,"domain_scores_codex":[0.9994715,0.00008447134,0.00006084787,0.0001033194,0.0001325579,0.0001472732],"domain_scores_gemma":[0.9995213,0.0001490642,0.0000187616,0.00002181067,0.0002229822,0.00006605484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009694039,0.00008030627,0.0272574,0.00006395128,0.00005703076,0.000002660986,0.4196132,0.000001735682,0.03446545,0.3382168,0.1025515,0.07759307],"study_design_scores_gemma":[0.001087545,0.0004223463,0.1426651,0.00002977862,0.00004713453,4.56594e-7,0.2051648,0.001166002,0.001829956,0.03143501,0.615718,0.0004338505],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9457955,0.0001557527,0.00138079,0.02884855,0.0001983625,0.0004425069,0.000003644605,0.00009399897,0.0230809],"genre_scores_gemma":[0.9965459,0.00004576582,0.0001686352,0.0001463985,0.0005801599,0.0000450166,0.000005350294,0.000003424904,0.002459382],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5131665,"threshold_uncertainty_score":0.5655946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4783140678417173,"score_gpt":0.541783498152382,"score_spread":0.06346943031066477,"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."}}