{"id":"W3120916333","doi":"10.1002/asi.24582","title":"Does double‐blind peer review reduce bias? Evidence from a top computer science conference","year":2021,"lang":"en","type":"preprint","venue":"Journal of the Association for Information Science and Technology","topic":"Academic integrity and plagiarism","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Catastrophic Loss Reduction","keywords":"Prestige; Double blind; Peer review; Quality (philosophy); Publication bias; Psychology; Limiting; Computer science; MEDLINE; Political science; Medicine; Alternative medicine; Law","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03003012,0.0001498437,0.0004176853,0.0006677967,0.001423914,0.001198637,0.002579666,0.0008366561,0.00002614562],"category_scores_gemma":[0.04205874,0.00008823904,0.0001316057,0.00242434,0.001622409,0.005007811,0.0009039448,0.001996432,0.000005012862],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001100498,"about_ca_system_score_gemma":0.007145603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003272788,"about_ca_topic_score_gemma":0.00009531499,"domain_scores_codex":[0.9945517,0.000130402,0.0009500742,0.0002488587,0.003727031,0.0003919509],"domain_scores_gemma":[0.9747635,0.0005359564,0.003284533,0.0003343239,0.02096685,0.000114901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005228224,0.0002338166,0.04997192,0.001809844,0.0005344373,0.000005596875,0.187743,0.0003216143,0.003479904,0.2620117,0.1197022,0.3736632],"study_design_scores_gemma":[0.004124644,0.0002798151,0.008097949,0.02554985,0.0008547084,0.00005817935,0.03369534,0.004208839,0.02957363,0.1218491,0.7701024,0.001605517],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3206908,0.002557989,0.007301684,0.6499375,0.01316733,0.002169851,0.0001007568,0.00009852544,0.003975595],"genre_scores_gemma":[0.9767138,0.01402412,0.005338071,0.002667176,0.0005400876,0.00003161114,0.000008339886,0.000004869164,0.000671917],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.656023,"threshold_uncertainty_score":0.9998761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09497121518925992,"score_gpt":0.3809406796166385,"score_spread":0.2859694644273786,"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."}}