{"id":"W2769975940","doi":"","title":"Online vs offline inequality:Examining disableist infrastructure via open data","year":2017,"lang":"en","type":"article","venue":"Lancaster EPrints (Lancaster University)","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Transpose; Open data; Online and offline; Computer science; Inequality; Abstraction; Internet privacy; Data science; World Wide Web; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01635915,0.0003479679,0.0005375727,0.004610476,0.003427505,0.009094611,0.001531008,0.001344339,0.009022313],"category_scores_gemma":[0.1011811,0.0003341422,0.0004691578,0.01015835,0.008365871,0.01235585,0.01106376,0.002835262,0.0006511345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003368306,"about_ca_system_score_gemma":0.002703763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.031654,"about_ca_topic_score_gemma":0.03748029,"domain_scores_codex":[0.9771386,0.01425575,0.001142702,0.002092804,0.003563887,0.001806161],"domain_scores_gemma":[0.8918221,0.07923663,0.01190458,0.008952493,0.006377528,0.001706657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004903353,0.0003584686,0.3993668,0.001545906,0.0003254944,0.0004733874,0.2180999,0.002014904,0.0006575919,0.2494932,0.0116287,0.1155454],"study_design_scores_gemma":[0.00004767226,0.0002522088,0.2974251,0.004330568,0.0002254234,0.0003446519,0.4177964,0.008056865,0.002360205,0.1399921,0.1289668,0.0002020258],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8414793,0.002015623,0.04351472,0.01850666,0.0002416108,0.0004793143,0.01043132,0.00006938566,0.08326203],"genre_scores_gemma":[0.988776,0.0004362217,0.005706336,0.0007660684,0.00008322881,0.0005837061,0.001776411,0.00006985981,0.001802273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.031654,"threshold_uncertainty_score":0.0865165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1410486580748379,"score_gpt":0.3566865876321027,"score_spread":0.2156379295572648,"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."}}