{"id":"W1870076635","doi":"10.3138/topia.26.195","title":"Cinematic Mourning: Partition and Indian Cinema","year":2011,"lang":"en","type":"article","venue":"TOPIA Canadian Journal of Cultural Studies","topic":"South Asian Cinema and Culture","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Movie theater; Partition (number theory); Art; History; Art history; Mathematics; Combinatorics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0009372007,0.0003714095,0.0002280646,0.001682398,0.01038832,0.01056937,0.00103138,0.0008983467,0.00854513],"category_scores_gemma":[0.003941642,0.0002118648,0.0002196386,0.001480371,0.009453559,0.002583886,0.004185099,0.00254866,0.0004042673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003667665,"about_ca_system_score_gemma":0.002566327,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03310245,"about_ca_topic_score_gemma":0.05979843,"domain_scores_codex":[0.9990156,0.0004514213,0.00001959849,0.00007743121,0.000119484,0.0003164375],"domain_scores_gemma":[0.9981036,0.0006607393,0.0002762341,0.000156778,0.000207955,0.0005946218],"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":[0.00026803,0.0001357308,0.01231232,0.0001974971,0.00002178373,0.0005970231,0.8111351,0.0001003025,0.002502319,0.1285929,0.004986258,0.03915079],"study_design_scores_gemma":[0.00001660536,0.00007496747,0.05126054,0.0003123536,0.00005555421,0.0006686049,0.8304597,0.0002427365,0.0007348502,0.007450872,0.1086765,0.0000466314],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5103297,0.004062001,0.0011171,0.004171219,0.0002973412,0.00005058224,0.00006297547,0.00005533419,0.4798537],"genre_scores_gemma":[0.9927098,0.0005291137,0.0001056289,0.0001514679,0.00006190507,0.000008843883,0.00001237073,0.00001877274,0.006402237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9668975,"threshold_uncertainty_score":0.06581956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1008162243731359,"score_gpt":0.2486374391305908,"score_spread":0.1478212147574549,"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."}}