{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003594277,0.0001618518,0.00021787,0.0001021425,0.0002242666,0.0003015233,0.0006334491,0.0002779868,0.0326369],"category_scores_gemma":[0.00006315309,0.0001733474,0.00009960041,0.0001295235,0.0001428091,0.001224092,0.00007515778,0.0002197251,0.509496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001057641,"about_ca_system_score_gemma":0.0001966331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001372084,"about_ca_topic_score_gemma":0.001879886,"domain_scores_codex":[0.9988844,0.00003200925,0.0002327312,0.0002485519,0.000285777,0.0003165341],"domain_scores_gemma":[0.999074,0.00006839,0.0001251516,0.0005710137,0.00003884464,0.0001225954],"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":[0.000008984928,0.00002757899,0.000002265501,0.00004145412,0.00002099915,0.000005321252,0.0001635802,0.000002447249,3.650817e-8,0.00180779,0.9966953,0.00122424],"study_design_scores_gemma":[0.0001538314,0.00001281147,0.000004856135,0.00004616946,0.00003389258,5.42379e-7,0.0003662625,0.000001417671,6.487832e-7,0.0002924651,0.9988669,0.0002202205],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000004494491,6.850742e-7,0.00001542269,0.00002039392,0.001343237,0.0002826609,0.9268736,0.00003869451,0.07142077],"genre_scores_gemma":[0.00002656484,0.0005773554,0.00002409762,0.0004153447,0.000497845,0.0000113287,0.9928012,0.000007773774,0.005638447],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4768592,"threshold_uncertainty_score":0.9682474,"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."}}