{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004011719,0.0001841178,0.0008853432,0.001312058,0.0006210497,0.0001012842,0.0004968181,0.0003574463,0.00008500888],"category_scores_gemma":[0.004416278,0.0001420389,0.0004081701,0.002060926,0.0009473412,0.0001277142,0.0000422413,0.001543498,0.000001810682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001628771,"about_ca_system_score_gemma":0.001119672,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1840776,"about_ca_topic_score_gemma":0.4916274,"domain_scores_codex":[0.9964298,0.001887914,0.0005870673,0.0002880524,0.000324784,0.000482396],"domain_scores_gemma":[0.9978743,0.0007392755,0.0005549878,0.0002454414,0.0003400538,0.0002459753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.000009784668,0.00003731081,0.7263448,0.00003261057,0.007113118,0.00002576773,0.2325684,0.001149727,0.0001365298,0.002822449,0.0003604292,0.0293991],"study_design_scores_gemma":[0.0006771646,0.0002077286,0.04246984,0.0001136321,0.01702096,0.00006911894,0.7801169,0.0004342679,0.0008782235,0.001989118,0.1554124,0.0006106385],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8381616,0.004388338,0.0003785709,0.1558026,0.00009829102,0.0001147551,0.000002293725,0.00002774103,0.001025774],"genre_scores_gemma":[0.9978149,0.000157524,0.0003439217,0.0001269332,0.00007093624,0.000005329106,9.101756e-7,0.00001564001,0.00146392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6838749,"threshold_uncertainty_score":0.8213557,"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."}}