{"id":"W4211218060","doi":"10.1016/b978-0-12-815874-6.00013-7","title":"Ethnographic and field data in historical economics","year":2021,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Culture, Economy, and Development Studies","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"","keywords":"Ethnography; Scope (computer science); Field (mathematics); Data science; Work (physics); Data collection; Epistemology; Positive economics; Social science; Sociology; Geography; Computer science; Economics; Engineering; Archaeology","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":[],"consensus_categories":[],"category_scores_codex":[0.0004398363,0.0002011614,0.0004392827,0.0001212248,0.0002625715,0.00006872181,0.0003379479,0.0003313173,0.0001595777],"category_scores_gemma":[0.00007260377,0.0002128917,0.0000662505,0.00001531828,0.0001549145,0.0000740111,0.0003940644,0.0003352575,0.00001367477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002359567,"about_ca_system_score_gemma":0.0002350244,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000081329,"about_ca_topic_score_gemma":0.03038255,"domain_scores_codex":[0.9988087,0.00002730494,0.0003306957,0.0005058849,0.0001020488,0.000225388],"domain_scores_gemma":[0.9992157,0.0001466837,0.0001232625,0.0003878669,0.00003329895,0.00009316263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003839415,0.000005923987,0.0009175038,0.00002823604,0.00007497082,0.00001974362,0.00484105,2.25239e-8,6.561992e-8,0.03510066,0.003359836,0.9556481],"study_design_scores_gemma":[0.0001030145,0.00000797773,0.00008959633,0.00008295796,0.00002359293,8.22407e-7,0.0003989997,2.891562e-7,3.032275e-7,0.008247802,0.9907849,0.0002596946],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0001237967,0.006503763,4.504275e-7,0.001154649,0.0005359242,0.0001875378,0.00001446411,0.00002233421,0.9914571],"genre_scores_gemma":[0.0004220522,0.01739963,0.0001665816,0.0007368276,0.0003941757,0.00001122632,0.00003159537,0.00001706803,0.9808208],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9874251,"threshold_uncertainty_score":0.9873105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06946651357593124,"score_gpt":0.2876178800933147,"score_spread":0.2181513665173834,"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."}}