{"id":"W4251909731","doi":"10.1142/p981","title":"Exploring Big Historical Data","year":2014,"lang":"en","type":"book","venue":"IMPERIAL COLLEGE PRESS eBooks","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Carleton University","funders":"","keywords":"Big data; History; Computer science; Data mining","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001676229,0.0003232319,0.0006994303,0.0001751563,0.0006887456,0.0001629626,0.001613407,0.0003204962,0.0000997285],"category_scores_gemma":[0.0004981976,0.0003293525,0.0002282566,0.00004423105,0.0002139844,0.0001561968,0.0007519909,0.0003910275,0.00003123391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000925234,"about_ca_system_score_gemma":0.001449245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003580117,"about_ca_topic_score_gemma":0.002008956,"domain_scores_codex":[0.9965826,0.0006880765,0.0005373327,0.0007672058,0.0009987358,0.0004260258],"domain_scores_gemma":[0.9974359,0.0008667222,0.0002995917,0.000916803,0.0002065336,0.0002743961],"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":[0.0001185523,0.00003450274,0.000001045287,0.00006974395,0.0002840412,0.00004492692,0.003545606,0.00001079192,0.00002191188,0.5092502,0.330179,0.1564396],"study_design_scores_gemma":[0.0002452094,0.00002533981,0.000001495735,0.00004618697,0.000178944,0.000001103036,0.00002460878,0.0001156718,0.000007841228,0.01266673,0.9863007,0.000386145],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00003803726,0.0004490459,0.001314253,0.0001389877,0.006577002,0.0003902168,0.0002137793,0.0001783022,0.9907004],"genre_scores_gemma":[0.0006508743,0.00009653581,0.001569188,0.00006678184,0.0149137,0.00007964166,0.0001330169,0.0000559319,0.9824343],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.6561217,"threshold_uncertainty_score":0.9999158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.485653745540465,"score_gpt":0.3824765685860768,"score_spread":0.1031771769543882,"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."}}