{"id":"W4409791077","doi":"10.61091/jcmcc127a-336","title":"Research on automatic generation and personalized matching system of ideological and political education content based on intelligent technology","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Ideological and Political Education","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ideology; Matching (statistics); Content (measure theory); Politics; Computer science; Political education; Personalized learning; Multimedia; Mathematics education; Political science; Psychology; Teaching method; Mathematics; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00331531,0.0001403538,0.0004848179,0.0004461519,0.0004929077,0.000126411,0.0001719614,0.0002424636,0.000005170478],"category_scores_gemma":[0.002111353,0.0001101588,0.00006066813,0.0003634877,0.0005958698,0.00005860534,0.00008365663,0.0004745364,6.603547e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002328627,"about_ca_system_score_gemma":0.0004855438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001153528,"about_ca_topic_score_gemma":0.000001317521,"domain_scores_codex":[0.9976591,0.0005375739,0.0007255023,0.0001820726,0.0005760088,0.000319766],"domain_scores_gemma":[0.9964742,0.002153688,0.0003243318,0.0001106113,0.0007507116,0.0001864377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005197736,0.0005069709,0.0003162786,0.0002367263,0.0000195965,0.000001660864,0.0008089892,0.000004213931,0.0001488127,0.995356,0.00003672158,0.002512065],"study_design_scores_gemma":[0.001542235,0.001343802,0.0005844347,0.001526982,0.00006777304,0.00001193287,0.01986584,0.007868705,0.0007855216,0.965999,0.0002466672,0.0001570891],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875098,0.0001889098,0.001377455,0.003701539,0.003933684,0.0003251572,7.584142e-7,0.00002372815,0.002938938],"genre_scores_gemma":[0.9977569,0.0000151296,0.001538547,0.00009807617,0.0005632377,0.000005156417,6.523538e-7,0.00000635193,0.0000159666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02935697,"threshold_uncertainty_score":0.4492145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.069735291403146,"score_gpt":0.3869610421582355,"score_spread":0.3172257507550895,"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."}}