{"id":"W2614545665","doi":"10.1101/139063","title":"Modeling science trustworthiness under publish or perish pressure","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Academic integrity and plagiarism","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Trustworthiness; Incentive; Publish or perish; Diligence; Publication; Value (mathematics); Psychology; Research integrity; Political science; Public relations; Computer science; Social psychology; Publishing; Economics; Law","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":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01759542,0.0009322749,0.001148955,0.002314206,0.002205299,0.005736444,0.002356938,0.004213843,0.008793619],"category_scores_gemma":[0.1249496,0.0008568321,0.001055672,0.001445956,0.006220195,0.00527448,0.004019102,0.002720077,0.00106845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006583906,"about_ca_system_score_gemma":0.003168004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01303581,"about_ca_topic_score_gemma":0.006004398,"domain_scores_codex":[0.9932353,0.003958086,0.0002530806,0.001163694,0.0006114518,0.0007784502],"domain_scores_gemma":[0.874671,0.08957622,0.01996008,0.006572744,0.004886811,0.004333151],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009051342,0.0006253219,0.09359413,0.0002963163,0.0003081941,0.00121948,0.005130708,0.4348152,0.001704595,0.432618,0.004716591,0.02406644],"study_design_scores_gemma":[0.0001828202,0.0004504666,0.01308073,0.0001034963,0.0001348173,0.0002746019,0.001397933,0.7358999,0.0003647458,0.2452773,0.002730135,0.0001029952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8427604,0.0005254604,0.119391,0.01230326,0.0001318494,0.0005060573,0.0007031966,0.0001984295,0.0234803],"genre_scores_gemma":[0.9929515,0.0001622502,0.003913804,0.0001348744,0.00004865469,0.0001060028,0.00007775916,0.00001880911,0.002586351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9957861,"threshold_uncertainty_score":0.09305459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03795160571044067,"score_gpt":0.2917620869855767,"score_spread":0.2538104812751361,"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."}}