{"id":"W2301078862","doi":"","title":"Social Balancing: No One Left Behind","year":2013,"lang":"en","type":"article","venue":"Divergent/Convergent","topic":"Community Development and Social Impact","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003401081,0.00024702,0.0004696102,0.0001377915,0.0005204891,0.0001525502,0.0004616123,0.0001561894,0.04257537],"category_scores_gemma":[0.0001043513,0.0003098656,0.0002543571,0.0001103336,0.00006921266,0.0004170605,0.0002938275,0.0002586924,0.03115096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000253946,"about_ca_system_score_gemma":0.00003966549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001883211,"about_ca_topic_score_gemma":0.00009762572,"domain_scores_codex":[0.998307,0.0000341802,0.000633416,0.0003438233,0.000109035,0.0005725165],"domain_scores_gemma":[0.999056,0.00003119895,0.0002688871,0.000310162,0.0001092439,0.0002245341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001802992,0.0005119855,0.8165973,0.00005591429,0.0004268623,0.000004208842,0.01115608,0.000001893658,0.0003527397,0.02038274,0.1486314,0.001860922],"study_design_scores_gemma":[0.0008343042,0.00006915924,0.7880155,0.00001129734,0.0000153037,9.424352e-7,0.0003083157,0.0002119042,0.0002345591,0.01416233,0.195453,0.0006833656],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9245498,0.000549184,0.0001568064,0.002124291,0.002306639,0.0004595967,0.00007974236,0.0001254654,0.06964844],"genre_scores_gemma":[0.9816072,0.0003138378,0.000195853,0.0006491576,0.0003479975,0.0000260701,0.00008930534,0.00003580897,0.01673477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05705736,"threshold_uncertainty_score":0.9999353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05563028273825171,"score_gpt":0.2383331962980975,"score_spread":0.1827029135598458,"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."}}