{"id":"W2337183287","doi":"10.1080/19312458.2016.1150975","title":"Augmenting Survey and Experimental Designs with Digital Trace Data","year":2016,"lang":"en","type":"article","venue":"Communication Methods and Measures","topic":"Survey Methodology and Nonresponse","field":"Social Sciences","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"TRACE (psycholinguistics); Computer science; Reliability (semiconductor); Data science; Survey research; Information retrieval; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.05110586,0.0000794338,0.0001354831,0.00003642685,0.0006022404,0.00009892642,0.000415887,0.00006750641,0.00002213416],"category_scores_gemma":[0.01351857,0.00005258719,0.00000941316,0.00009820741,0.0007175043,0.0004711218,0.000213598,0.00007374003,0.000001203153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001429563,"about_ca_system_score_gemma":0.00007566707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003651128,"about_ca_topic_score_gemma":0.0006915331,"domain_scores_codex":[0.9711623,0.02817306,0.0001375219,0.0002318969,0.0001353335,0.0001598683],"domain_scores_gemma":[0.9794073,0.01973125,0.00007828663,0.000617106,0.00007421926,0.00009188346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001881112,0.00009694818,0.1545506,0.000004339618,0.00007517757,9.994168e-7,0.008669922,1.557502e-7,0.02555023,0.001753236,0.0003074027,0.8071098],"study_design_scores_gemma":[0.001724635,0.0001745673,0.8626232,0.0001139186,0.00004442829,0.00001644573,0.01034783,0.0000226394,0.02016289,0.001964643,0.1022509,0.0005538839],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7220106,0.01424874,0.2588592,0.001485619,0.00007817151,0.0002656144,0.00004686709,0.00007504434,0.00293014],"genre_scores_gemma":[0.7854702,0.0008705559,0.2132631,0.0000375949,0.00001546378,0.000008824547,0.000008850478,0.000007456731,0.0003179554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.806556,"threshold_uncertainty_score":0.994791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6507050796077433,"score_gpt":0.5561634394579629,"score_spread":0.09454164014978039,"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."}}