{"id":"W4380087334","doi":"10.1017/awf.2023.39","title":"Using institutional ethnography to analyse animal sheltering and protection I: Animal protection work","year":2023,"lang":"en","type":"article","venue":"Animal Welfare","topic":"Geographies of human-animal interactions","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of British Columbia","funders":"","keywords":"Enforcement; Law enforcement; Work (physics); Psychological intervention; Animal welfare; Intervention (counseling); Law; Ethnography; Political science; Distress; Public relations; Business; Criminology; Law and economics; Sociology; Psychology; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005860991,0.0002596026,0.0002406708,0.000926732,0.003432818,0.0003867354,0.0002354002,0.0001985461,0.0002433265],"category_scores_gemma":[0.0002464296,0.0002911957,0.0001845904,0.003898151,0.0003786132,0.0009045272,0.0001944226,0.0004101363,0.0001101271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00016815,"about_ca_system_score_gemma":0.00006476495,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008519646,"about_ca_topic_score_gemma":0.009251837,"domain_scores_codex":[0.9976498,0.0001733553,0.0003514682,0.0006198594,0.0005617824,0.0006436755],"domain_scores_gemma":[0.9991411,0.00002770102,0.0001293212,0.0001852667,0.0002443017,0.0002722952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.007504556,0.0008386634,0.1249419,0.0004307796,0.001074981,0.0003652176,0.06198665,0.003040643,0.7147335,0.0608198,0.002148989,0.02211437],"study_design_scores_gemma":[0.0004185135,0.001218016,0.9210759,0.0001859867,0.0001102119,0.00003203782,0.03679851,0.00124495,0.0008411187,0.0002508039,0.03700331,0.0008205864],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897394,0.0000580579,0.00008477395,0.006744156,0.0002841873,0.0009808522,0.00001792781,0.0006004703,0.001490148],"genre_scores_gemma":[0.9982597,0.00001805417,0.0008964973,0.00007589635,0.0004481697,0.0001974097,0.000008192605,0.00003154113,0.00006453195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7961341,"threshold_uncertainty_score":0.999954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1005262159255663,"score_gpt":0.3593059115532528,"score_spread":0.2587796956276865,"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."}}