{"id":"W4379933303","doi":"10.1016/j.ssmqr.2023.100293","title":"Divided in a digital economy: Understanding disability employment inequities stemming from the application of advanced workplace technologies","year":2023,"lang":"en","type":"article","venue":"SSM - Qualitative Research in Health","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Institute for Work & Health; McMaster University; University of British Columbia; Public Health Ontario; BC Children's Hospital; University of Toronto","funders":"Canadian Arthritis Network; Social Sciences and Humanities Research Council of Canada; Arthritis Society","keywords":"Digital economy; Digital transformation; Work (physics); Equity (law); Public relations; Business; Engineering; Political science","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":[],"consensus_categories":[],"category_scores_codex":[0.02049046,0.000123355,0.0003449832,0.0001987735,0.0005093798,0.0001052707,0.0004994188,0.00008064301,0.000008818707],"category_scores_gemma":[0.003167467,0.0001012152,0.00005371415,0.00190459,0.002329707,0.0004151791,0.000286341,0.0004074892,0.00001223757],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004061743,"about_ca_system_score_gemma":0.0005859492,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0266691,"about_ca_topic_score_gemma":0.04200664,"domain_scores_codex":[0.9941393,0.002868768,0.0008130934,0.0004812037,0.0008333357,0.0008642634],"domain_scores_gemma":[0.9889231,0.0102964,0.0001907664,0.0004070055,0.00009751639,0.00008520593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.000097196,0.0002137136,0.2521573,0.0003275532,0.00001619681,5.259886e-7,0.4915986,0.0001136315,0.000008989949,0.2340513,0.0001495302,0.02126545],"study_design_scores_gemma":[0.0002001064,0.0000844276,0.008143672,0.0001897494,3.913873e-7,6.096271e-9,0.6204938,0.00005686338,0.00001060529,0.3703117,0.000446379,0.00006234045],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9558594,0.0004145812,0.0005228866,0.03842495,0.00006615865,0.002530358,0.00008578238,0.0001289252,0.00196694],"genre_scores_gemma":[0.9987547,0.0004482847,0.00007589326,0.00002392514,0.00002953089,0.0005690618,0.00002715612,0.0000107463,0.00006072452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2440136,"threshold_uncertainty_score":0.9997615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6842742289283648,"score_gpt":0.6199957660232356,"score_spread":0.06427846290512917,"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."}}