{"id":"W2961770978","doi":"10.1093/pastj/gtz017","title":"A Microhistory of the Global Empire of Cotton: Ivanovo, The ‘Russian Manchester’*","year":2019,"lang":"en","type":"article","venue":"Past & Present","topic":"Historical Studies and Socio-cultural Analysis","field":"Arts and Humanities","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Serfdom; History; Economic history","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":[0.0003776595,0.0001260372,0.00016139,0.000673146,0.003741464,0.001856735,0.0001574863,0.0003341346,0.001663258],"category_scores_gemma":[0.0003677818,0.0001501911,0.00006555866,0.0007813083,0.005378085,0.0008496644,0.001335159,0.0006800224,0.0001294385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003458618,"about_ca_system_score_gemma":0.0008582678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02008775,"about_ca_topic_score_gemma":0.03763065,"domain_scores_codex":[0.9997684,0.0001032527,0.000005721986,0.00004143333,0.00001779076,0.00006344286],"domain_scores_gemma":[0.9998541,0.00006324718,0.00003424924,0.00001229789,0.00001146856,0.00002474262],"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":[0.0001651231,0.00006861858,0.04992606,0.0002649173,0.00002232977,0.004126532,0.5827051,0.0008111286,0.004949543,0.3121992,0.004123392,0.04063803],"study_design_scores_gemma":[0.00001292903,0.0002686439,0.2307713,0.0005910777,0.00001800216,0.001695086,0.2137393,0.0004371897,0.001883699,0.0165987,0.5339447,0.00003934234],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9145756,0.004623255,0.0002741515,0.001090298,0.00005302806,0.000009555968,0.0000415033,0.000007572027,0.07932507],"genre_scores_gemma":[0.9968565,0.0005045473,0.00003322447,0.00003720574,0.000006877297,0.000001514313,0.000009207441,0.000003627919,0.002547317],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02008775,"threshold_uncertainty_score":0.03994167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01548683209554559,"score_gpt":0.2078994557728085,"score_spread":0.1924126236772629,"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."}}