{"id":"W1579310259","doi":"10.21432/t2mw29","title":"Things I Have Learned about Meta-Analysis Since 1990: Reducing Bias in Search of “The Big Picture” / Ce que j’ai appris sur la méta-analyse depuis 1990 : réduire les partis pris en quête d’une vue d’ensemble","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Learning and Technology","topic":"Teacher Education and Leadership Studies","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Psychology; Humanities; Meta-analysis; Population; Sociology; Philosophy; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.5819783,0.003205822,0.01391496,0.02096561,0.005462328,0.02217317,0.007051785,0.01500398,0.002207319],"category_scores_gemma":[0.800756,0.00445822,0.01417917,0.02959988,0.01912445,0.02786469,0.0107526,0.02384952,0.0007159263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01264863,"about_ca_system_score_gemma":0.02199407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008242195,"about_ca_topic_score_gemma":0.01154384,"domain_scores_codex":[0.3006966,0.5680531,0.07188601,0.01788601,0.03984813,0.001630162],"domain_scores_gemma":[0.1257419,0.7622073,0.0356375,0.04525483,0.029529,0.001629517],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001534183,0.0001253535,0.01458393,0.1868003,0.1056544,0.000702356,0.02730325,0.004371701,0.001210679,0.1237115,0.07182932,0.462173],"study_design_scores_gemma":[0.001206586,0.001243322,0.01031813,0.2635655,0.05638295,0.001537625,0.005421203,0.005168708,0.002948152,0.4219548,0.2290094,0.001243609],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.002965743,0.7076711,0.05233441,0.2193363,0.01476507,0.0005209539,0.0003511877,0.0002598508,0.001795529],"genre_scores_gemma":[0.1393824,0.4512601,0.2257364,0.155496,0.0207248,0.004768995,0.0004744875,0.0007178478,0.00143899],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4180217,"threshold_uncertainty_score":0.5154952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1190461968930597,"score_gpt":0.3522169063773506,"score_spread":0.233170709484291,"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."}}