{"id":"W4382393838","doi":"10.5964/meth.10863","title":"The logics of and strategies to enhance generalization of mixed methods research findings","year":2023,"lang":"en","type":"article","venue":"Methodology","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Generalization; Context (archaeology); Computer science; Interpretation (philosophy); Management science; Artificial intelligence; Psychology; Epistemology; Engineering; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["metaresearch"],"domain":"methods","study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.06531996,0.00005881168,0.000244071,0.0002981608,0.0006159256,0.000005658528,0.0002575468,0.0001085249,0.00005712152],"category_scores_gemma":[0.02855685,0.00004161413,0.00001565511,0.001579618,0.0003303905,0.00005949226,0.0002782138,0.0002581486,0.0000287795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003703755,"about_ca_system_score_gemma":0.0004562851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006245332,"about_ca_topic_score_gemma":0.0005270321,"domain_scores_codex":[0.9706994,0.02744172,0.000761506,0.0002248186,0.0002845697,0.0005880395],"domain_scores_gemma":[0.9509223,0.04804983,0.000190941,0.0002577582,0.0004916784,0.00008749812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001137887,0.000009144315,0.00971146,0.0006126165,0.00002004244,8.114279e-7,0.08108491,0.0001634353,0.4738549,0.3253349,0.0343583,0.07473566],"study_design_scores_gemma":[0.0005764427,0.0007854533,0.2836914,0.0001085229,0.00001617706,0.000002264245,0.103835,0.0009868292,0.1774094,0.2481913,0.1841623,0.0002348461],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6849713,0.00003834043,0.2999498,0.01341385,0.0003806274,0.0005752487,0.00001353432,0.00002585908,0.0006314362],"genre_scores_gemma":[0.192316,0.000488012,0.8031741,0.001914135,0.0001158397,0.0003168438,0.000004372834,0.00001814991,0.001652549],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5032243,"threshold_uncertainty_score":0.979626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9500912121363324,"score_gpt":0.8438520423046605,"score_spread":0.1062391698316719,"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."}}