{"id":"W3043073077","doi":"10.1111/maq.12577","title":"Veteran Therapeutics: The Promise of Military Medicine and the Possibilities of Disability in the Post‐9/11 United States","year":2020,"lang":"en","type":"article","venue":"Medical Anthropology Quarterly","topic":"Gender, Security, and Conflict","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Collateral damage; Anticipation (artificial intelligence); Ethnography; Collateral; Disability studies; Posttraumatic stress; Psychology; Psychotherapist; Medicine; Psychiatry; Psychoanalysis; Criminology; Sociology; Political science; Law; Gender studies","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":[0.001912598,0.0001969,0.0001613801,0.000718257,0.01239011,0.005257849,0.0003437141,0.001643723,0.002473729],"category_scores_gemma":[0.001648432,0.0001274155,0.0001306132,0.0004233593,0.02170124,0.003843029,0.004606383,0.003072057,0.000120048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003818065,"about_ca_system_score_gemma":0.00325525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009441532,"about_ca_topic_score_gemma":0.02228835,"domain_scores_codex":[0.9985694,0.0008971589,0.00002311477,0.00005325565,0.0001184133,0.0003386847],"domain_scores_gemma":[0.9992496,0.0003484194,0.0001580873,0.00002678473,0.00004630864,0.0001707114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003424504,0.00006194461,0.006028881,0.00005809843,0.000005906399,0.0005861516,0.5886126,0.00006147429,0.0004111502,0.3710322,0.007855906,0.0252514],"study_design_scores_gemma":[0.000004624839,0.0001219411,0.006309896,0.0002513542,0.00000513305,0.0007216312,0.8229716,0.00005971597,0.0001994483,0.02234723,0.1469875,0.00001986681],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7490941,0.02266401,0.0007696045,0.1211535,0.0007312163,0.00001240382,0.00003209723,0.00001486583,0.1055283],"genre_scores_gemma":[0.9929052,0.002459252,0.00007145573,0.002405984,0.0001032492,0.00000559804,0.000003557673,0.000002561064,0.002043144],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01239011,"threshold_uncertainty_score":0.02770215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04192533813210839,"score_gpt":0.3427791061990672,"score_spread":0.3008537680669588,"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."}}