{"id":"W2104367869","doi":"10.5539/hes.v1n2p73","title":"On Cultivation of Characteristic Talents in Law in Institutes of Technology","year":2011,"lang":"en","type":"article","venue":"Higher Education Studies","topic":"Legal Education and Practice Innovations","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"China; Competition (biology); Face (sociological concept); Resource (disambiguation); Law; Political science; Engineering ethics; Engineering; Sociology; Computer science; Social science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002003577,0.0001582345,0.0001369262,0.001723735,0.002502526,0.003205191,0.0004065085,0.0005537856,0.002140568],"category_scores_gemma":[0.00277862,0.00008065462,0.0002189778,0.002275725,0.006228133,0.002273129,0.002346134,0.0005990567,0.0001388456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004231555,"about_ca_system_score_gemma":0.004013199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002374663,"about_ca_topic_score_gemma":0.005624448,"domain_scores_codex":[0.9986564,0.0005562156,0.00003774124,0.0001263422,0.000277295,0.0003460856],"domain_scores_gemma":[0.9965979,0.00166655,0.0006747859,0.0002345502,0.0004212834,0.0004049349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008118482,0.0002806102,0.0822035,0.0001953404,0.00001569301,0.0007221379,0.01585196,0.0009386691,0.003529941,0.8026013,0.001099291,0.09248029],"study_design_scores_gemma":[0.00004349085,0.0009243402,0.4841334,0.0007745689,0.0001135736,0.001042639,0.04913927,0.004903005,0.01253218,0.3695808,0.07668994,0.0001229117],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8266745,0.001456394,0.005214954,0.005129454,0.00002584786,0.00005237292,0.00003031465,0.00001460457,0.1614015],"genre_scores_gemma":[0.997359,0.000732934,0.0005926092,0.00009750506,0.00001115092,0.000009180869,0.000006973994,9.349297e-7,0.001189684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004231555,"threshold_uncertainty_score":0.03070223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1557457543246269,"score_gpt":0.4454658073598526,"score_spread":0.2897200530352257,"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."}}