{"id":"W4403103800","doi":"10.1111/var.12334","title":"Real cameras, irreal things: Image‐making and ethnographic insight","year":2024,"lang":"en","type":"article","venue":"Visual Anthropology Review","topic":"Museums and Cultural Heritage","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Society for Visual Anthropology; Association for Slavic, East European, and Eurasian Studies; Wenner-Gren Foundation","keywords":"Ethnography; Computer vision; Artificial intelligence; Computer science; Art; Visual arts; Aesthetics; Sociology; Anthropology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01777845,0.0004309626,0.0004150021,0.00554837,0.003207921,0.006795729,0.001474495,0.00154098,0.003294609],"category_scores_gemma":[0.01913293,0.0004113394,0.0002725917,0.003649505,0.0323066,0.01280683,0.005754752,0.001740492,0.000191247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002300828,"about_ca_system_score_gemma":0.00152193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00277253,"about_ca_topic_score_gemma":0.005632845,"domain_scores_codex":[0.9760164,0.02101646,0.0003937979,0.0008682147,0.00127624,0.0004288384],"domain_scores_gemma":[0.9616069,0.03487021,0.001248936,0.001370698,0.000699412,0.0002039398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003346576,0.00007120927,0.002885604,0.001263638,0.00002268453,0.0005241997,0.8081191,0.0001680728,0.0006854493,0.1272112,0.002206281,0.05680912],"study_design_scores_gemma":[0.00001490447,0.00006148004,0.005064497,0.003374713,0.00002358281,0.0008623,0.8369689,0.0002575344,0.0009710146,0.03574973,0.1166177,0.00003359845],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6727077,0.09252548,0.03361576,0.02589189,0.0009245867,0.0003712037,0.0002012636,0.00008116616,0.173681],"genre_scores_gemma":[0.9602906,0.02829738,0.005813291,0.001275278,0.000282209,0.0001744798,0.00003595173,0.00003948081,0.003791315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01777845,"threshold_uncertainty_score":0.09402251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0424706227401492,"score_gpt":0.3581187644990189,"score_spread":0.3156481417588697,"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."}}