{"id":"W4292451703","doi":"10.3390/recycling7040053","title":"Unpicking the Gender Gap: Examining Socio-Demographic Factors and Repair Resources in Clothing Repair Practice","year":2022,"lang":"en","type":"article","venue":"Recycling","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Social Sciences and Humanities Research Council of Canada; Mitacs; University of Alberta","keywords":"Clothing; Context (archaeology); Enabling; Reuse; Consumption (sociology); Business; Product (mathematics); Engineering; Psychology; Sociology; Political science; Geography; Waste management","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005400661,0.0002862395,0.0002769103,0.0007248761,0.001551178,0.0004047794,0.0003848135,0.00006479375,0.0001030425],"category_scores_gemma":[0.001492299,0.0002472794,0.0001511492,0.001412926,0.00008601763,0.001266518,0.00133705,0.0007663211,0.000005834392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002151117,"about_ca_system_score_gemma":0.00002361055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001707902,"about_ca_topic_score_gemma":0.00007401122,"domain_scores_codex":[0.9972997,0.00024424,0.0005305957,0.0006601936,0.0006437585,0.0006215038],"domain_scores_gemma":[0.9978877,0.001105169,0.0004480491,0.0004628282,0.00007839946,0.00001779752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005543087,0.00007771099,0.975132,0.0003775141,0.0001449182,0.0001893008,0.008231696,0.006437524,0.00003318789,0.006248315,0.001178798,0.001893622],"study_design_scores_gemma":[0.0008031661,0.00002566986,0.1869285,0.00009258378,0.0002280247,0.00001300633,0.3547442,0.02521863,0.000002678206,0.004071902,0.4272014,0.00067022],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815106,0.001213573,0.0001084893,0.001537126,0.000414237,0.0005772975,5.754716e-7,0.0006918216,0.01394625],"genre_scores_gemma":[0.9951887,0.00005418966,0.0006887101,0.003083222,0.0005186871,0.00009305023,0.000008900207,0.00007264925,0.0002919556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7882035,"threshold_uncertainty_score":0.999998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04030292654677536,"score_gpt":0.2548562602893829,"score_spread":0.2145533337426075,"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."}}