{"id":"W4398356832","doi":"10.7910/dvn/8ywcf9/lwl0sm","title":"cgss.tab","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Digital Economy and Work Transformation","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Crowdsourcing; China; Face (sociological concept); The Internet; Data science; Business; Computer science; World Wide Web; Sociology; Political science; Social 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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004246947,0.0001698545,0.0002280276,0.000108066,0.0002388246,0.0002963908,0.0006578948,0.0002945804,0.03255948],"category_scores_gemma":[0.0001024531,0.0001823538,0.0001043808,0.0001417158,0.0001571759,0.001317469,0.0000826082,0.000240658,0.5041657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001126045,"about_ca_system_score_gemma":0.0003243645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0013076,"about_ca_topic_score_gemma":0.001795509,"domain_scores_codex":[0.9987821,0.00007347956,0.0002426766,0.000262201,0.0003070293,0.0003325172],"domain_scores_gemma":[0.9990385,0.00007322716,0.0001306247,0.0005879509,0.00004054454,0.0001292062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009987409,0.00002916759,0.000002911489,0.00005550213,0.00002170079,0.000005753219,0.0001644944,0.000002654795,2.778927e-8,0.001098822,0.9971907,0.001418315],"study_design_scores_gemma":[0.0001636501,0.00001404302,0.000004420049,0.00006611036,0.00003406828,5.789702e-7,0.0003412041,0.000001469506,5.624219e-7,0.0001234592,0.9990191,0.0002313293],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000003633979,7.708642e-7,0.00001805719,0.00002384927,0.001377216,0.0002960826,0.9224388,0.00004074776,0.07580084],"genre_scores_gemma":[0.00003267945,0.0006637167,0.00002576345,0.0004309667,0.0005402195,0.00001211121,0.9952017,0.00000818337,0.003084666],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4716062,"threshold_uncertainty_score":0.9683249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02292927640390565,"score_gpt":0.2678653922686492,"score_spread":0.2449361158647435,"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."}}