{"id":"W4398399548","doi":"10.7910/dvn/8ywcf9/plxdlk","title":"abs.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; Business; World Wide Web; Computer science; Political science; Sociology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001482743,0.003264509,0.00235774,0.005783391,0.001152663,0.006540916,0.003948419,0.00339157,0.351243],"category_scores_gemma":[0.01175162,0.001123964,0.00171566,0.01018738,0.0008939983,0.00311277,0.003658616,0.002839417,0.4089667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002100016,"about_ca_system_score_gemma":0.002669063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01560054,"about_ca_topic_score_gemma":0.01974444,"domain_scores_codex":[0.9984753,0.000266132,0.000210422,0.0004938654,0.0002829011,0.00027135],"domain_scores_gemma":[0.9961792,0.001258562,0.0003847649,0.00098395,0.0007394659,0.0004540263],"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":[0.00003289843,0.00001238435,0.0002480888,0.0005918313,0.00002114037,0.00001105248,0.0000136656,0.00009737662,0.00005215486,0.0003644648,0.9976271,0.0009278141],"study_design_scores_gemma":[0.0003953431,0.00002255847,0.001780935,0.0005326777,0.00003436326,0.00004971401,0.00008133406,0.0002815733,0.0002891965,0.001863212,0.9946395,0.00002963468],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003874789,0.00004946482,0.00002813879,0.00007140897,0.00002900091,0.000006244849,0.9987931,0.0003479082,0.0006359822],"genre_scores_gemma":[0.0003158311,0.00008270294,0.0001243287,0.000105968,0.00001698422,0.00006374936,0.9980572,0.0001959348,0.001037305],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.648757,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02224957929475305,"score_gpt":0.2677300930536552,"score_spread":0.2454805137589022,"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."}}