{"id":"W2887998895","doi":"10.7554/elife.29319","title":"Motivating participation in open science by examining researcher incentives","year":2017,"lang":"en","type":"article","venue":"eLife","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; Genome Alberta; Genome Canada","keywords":"Open science; Incentive; Thematic analysis; Public relations; Science policy; Open data; Intellectual property; Political science; Qualitative research; Sociology; Psychology; Social science; Public administration; Economics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics","sts","scholarly_communication","open_science"],"consensus_categories":["metaresearch","bibliometrics"],"category_scores_codex":[0.07643422,0.00008600432,0.0001956041,0.01630457,0.001435487,0.01625065,0.0110609,0.00004919707,0.0003291153],"category_scores_gemma":[0.2732339,0.00006349556,0.00002244144,0.0548518,0.0007795496,0.003882953,0.006869468,0.0002577397,0.0002499857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001627916,"about_ca_system_score_gemma":0.0003993214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008791034,"about_ca_topic_score_gemma":0.00007614627,"domain_scores_codex":[0.987984,0.0002584797,0.0005099533,0.0008413275,0.009642397,0.0007638594],"domain_scores_gemma":[0.9933856,0.002397455,0.0003416631,0.001404655,0.002074493,0.0003961821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009298966,0.00008101091,0.8641995,8.403667e-7,0.000001192753,0.000004233864,0.0003076261,0.000007994374,0.009055301,0.0004401864,0.005046217,0.1208466],"study_design_scores_gemma":[0.000352518,0.00005560899,0.977972,0.00001514513,3.274941e-7,2.052453e-7,0.000503239,0.005944079,0.01099678,0.001258868,0.002810664,0.00009058215],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9625258,0.00008962285,0.0003768157,0.001045501,0.0001835535,0.0002457227,0.000007146835,0.000007154553,0.03551864],"genre_scores_gemma":[0.9964773,0.00003496202,0.0008646605,0.00007570779,0.00003674984,0.00002174807,0.000001048492,0.000005932051,0.002481868],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1967997,"threshold_uncertainty_score":0.9998645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8882448692560714,"score_gpt":0.7166902763157139,"score_spread":0.1715545929403575,"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."}}