{"id":"W4415256959","doi":"10.1145/3757454","title":"Accessibility Work in Academia: Balancing Needs, Bridging Gaps, and Breaking Down Barriers","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Assistive Technology in Communication and Mobility","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Bridging (networking); Work (physics); Web accessibility; Government (linguistics); Intersection (aeronautics); Inclusion (mineral)","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":"codex-gemma-dda1882f352a","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001274747,0.000170559,0.0002976017,0.000368412,0.0007277665,0.00002828424,0.001236792,0.0005263551,0.00003526973],"category_scores_gemma":[0.001449807,0.0001371829,0.00006707452,0.0006320768,0.0001641115,0.0003262786,0.00196795,0.003303609,0.000003803426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004593822,"about_ca_system_score_gemma":0.00005393116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001017927,"about_ca_topic_score_gemma":0.0000270456,"domain_scores_codex":[0.9984578,0.0001010829,0.0006680472,0.0003517509,0.0001445382,0.0002767407],"domain_scores_gemma":[0.9980497,0.0005587706,0.0005310623,0.0006097527,0.0002068178,0.00004391817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001550286,0.00005316348,0.9661359,0.0003192025,0.00003008525,9.403099e-8,0.00229565,0.000005602509,0.003234767,0.003159579,0.007147585,0.01746335],"study_design_scores_gemma":[0.000623752,0.00002994536,0.9832437,0.002840443,0.00002320386,0.000001185699,0.002790919,0.0005358823,0.002788783,0.004545453,0.002440763,0.0001359963],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9861777,0.00005771349,0.0001523917,0.007518187,0.0006400899,0.0005703773,0.000001497144,0.0001378322,0.004744234],"genre_scores_gemma":[0.9970148,0.00002211313,0.0008848747,0.001651571,0.00008587624,0.0000869675,0.000001382777,0.00001078469,0.0002416374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01732735,"threshold_uncertainty_score":0.9989958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05578941888617848,"score_gpt":0.4306027081112403,"score_spread":0.3748132892250618,"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."}}