{"id":"W1530909213","doi":"10.5204/mcj.746","title":"Smooth Effects: The Erasure of Labour and Production of Police as Experts through Augmented Objects","year":2013,"lang":"en","type":"article","venue":"M/C Journal","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Erasure; Production (economics); Sociology; Labour economics; Computer science; Computer security; Business; Economics; Microeconomics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005701735,0.001401369,0.0004921826,0.002948086,0.004280283,0.01056124,0.002481397,0.002205611,0.03623819],"category_scores_gemma":[0.02098465,0.001331702,0.0009523064,0.001402589,0.00786336,0.0117351,0.02270036,0.001843043,0.006127873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001364516,"about_ca_system_score_gemma":0.002085114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003947559,"about_ca_topic_score_gemma":0.007554137,"domain_scores_codex":[0.9951329,0.002656911,0.0001480795,0.0005646212,0.001039767,0.0004577824],"domain_scores_gemma":[0.9898667,0.004160328,0.0003635141,0.003979116,0.0009777946,0.000652537],"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":[0.000783263,0.0002534131,0.006080987,0.000921352,0.00007904605,0.001282794,0.1403609,0.003891887,0.01353408,0.1192775,0.0946132,0.6189215],"study_design_scores_gemma":[0.0001121257,0.0003249595,0.004493311,0.0008884753,0.00009176241,0.0006554516,0.03724288,0.007069816,0.004870016,0.0690668,0.8750156,0.0001687437],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1289438,0.003740441,0.447833,0.01687986,0.003380253,0.0009573784,0.001559616,0.01155303,0.3851526],"genre_scores_gemma":[0.6019254,0.00261382,0.2896205,0.002065871,0.001136302,0.0009597,0.001077126,0.004636314,0.09596501],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03623819,"threshold_uncertainty_score":0.1212288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004779951630128199,"score_gpt":0.2164254904151786,"score_spread":0.2116455387850504,"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."}}