{"id":"W2909228053","doi":"","title":"How to make sense: Sensory modification in grinder subculture","year":2018,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ethnography; Agency (philosophy); Situated; Participant observation; Sociology; Variety (cybernetics); Engineering; Computer science; Social science; Artificial intelligence; Anthropology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006167068,0.0003978655,0.0004047356,0.004179259,0.0006521908,0.0005351253,0.001704393,0.0008213656,0.00001069759],"category_scores_gemma":[0.0001798475,0.0004618,0.0001341951,0.003810777,0.0002229295,0.0008749759,0.0003597026,0.002221261,0.0001552587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001952996,"about_ca_system_score_gemma":0.0006467617,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001858203,"about_ca_topic_score_gemma":0.02435524,"domain_scores_codex":[0.995774,0.0006193124,0.0003179204,0.001429104,0.0009498005,0.0009098384],"domain_scores_gemma":[0.9970412,0.0001302247,0.0002810855,0.001393371,0.0009698624,0.0001842246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002505067,0.001004679,0.01356778,0.0006330909,0.0007716442,0.0149036,0.01773227,0.00006758489,0.5342504,0.3668533,0.03027254,0.017438],"study_design_scores_gemma":[0.00157315,0.001396636,0.1403346,0.0005693315,0.00004472461,0.0002848799,0.01464401,0.001567227,0.5884164,0.004842504,0.2443109,0.002015649],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.911798,0.00003435612,0.003524479,0.003008894,0.00210195,0.001012468,0.000006313686,0.0003454477,0.07816809],"genre_scores_gemma":[0.8057243,0.00001779521,0.0004907455,0.0000242107,0.0003461501,0.00001287807,0.00006759895,0.00003642755,0.1932799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3620108,"threshold_uncertainty_score":0.9997834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04448423756458426,"score_gpt":0.3114417555959047,"score_spread":0.2669575180313205,"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."}}