{"id":"W2507166707","doi":"10.1037/cap0000056","title":"Increasing literacy in quantitative methods: The key to the future of Canadian psychology.","year":2016,"lang":"en","type":"article","venue":"Canadian Psychology/Psychologie canadienne","topic":"Statistics Education and Methodologies","field":"Mathematics","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"National Center for Research Resources; Social Sciences and Humanities Research Council of Canada; National Institutes of Health","keywords":"Psychology; Literacy; Information literacy; Engineering ethics; Medical education; Applied psychology; Management science; Pedagogy","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","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.007283268,0.0004808634,0.0006731611,0.002590383,0.0003683291,0.00005316178,0.00198711,0.0004749983,0.0006879802],"category_scores_gemma":[0.01188582,0.0002766963,0.0001536819,0.002858787,0.0007319065,0.0001257814,0.000055066,0.0006743397,0.0001529192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007894398,"about_ca_system_score_gemma":0.001230542,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5725034,"about_ca_topic_score_gemma":0.9919208,"domain_scores_codex":[0.9930783,0.002891386,0.0009929724,0.0009816501,0.0001619443,0.00189377],"domain_scores_gemma":[0.9899867,0.005705256,0.0003471305,0.002139708,0.0004240297,0.001397168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002137794,0.00007833349,0.008844591,0.00002246616,0.0001454894,0.0001302609,0.01299512,0.000002229201,0.001151591,0.2026237,0.5119408,0.2618517],"study_design_scores_gemma":[0.0007190778,0.000201601,0.1298213,0.00009793879,0.00003796311,0.0001998818,0.002910942,0.000002829042,0.00003838547,0.1064263,0.7591266,0.0004171787],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.4169081,0.002691336,0.01539752,0.5056751,0.01704727,0.00271105,0.001979782,0.0001334939,0.03745636],"genre_scores_gemma":[0.4411818,0.001222667,0.4809172,0.07275597,0.0007755405,0.0006299028,0.00004010849,0.0001906733,0.00228609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4655197,"threshold_uncertainty_score":0.9999685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2380091746887879,"score_gpt":0.5046286997589575,"score_spread":0.2666195250701697,"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."}}