{"meta":{"query_hash":"54f592fec7ef","filters":{"venue":"기술혁신연구"},"cohort_total":3,"direct_labels_cover":0,"predictions_cover":3,"exported":3,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/54f592fec7ef","api":"https://metacan.xera.ac/api/v1/cohort?venue=%EA%B8%B0%EC%88%A0%ED%98%81%EC%8B%A0%EC%97%B0%EA%B5%AC"},"results":[{"id":"W2273293775","doi":"","title":"비모수적 방법을 이용한 OECD 국가별 R&D 효율성과 생산성 분석","year":2003,"lang":"ko","type":"article","venue":"기술혁신연구","topic":"Engineering Applied Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Data envelopment analysis; Productivity; Econometrics; Economics; Technical change; Malmquist index; Technological change; Stock (firearms); Total factor productivity; Sample (material); Technical progress; Mathematics; Statistics; Macroeconomics; Geography","score_opus":0.010383459062494953,"score_gpt":0.23367476003202647,"score_spread":0.2232913009695315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2273293775","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7533418,0.012517114,0.012574538,0.0017910132,0.00018104122,0.000133782,0.045298457,0.0008244291,0.17333777],"genre_scores_gemma":[0.9090554,0.01235787,0.011767191,0.00037792724,0.00010655201,0.00019884907,0.034884702,0.0001434175,0.031108035],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978123,0.000216844,0.00015324849,0.00027572605,0.0012918579,0.0002499904],"domain_scores_gemma":[0.991338,0.0013797664,0.0025341471,0.00079123036,0.0037254775,0.0002313527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022448932,0.0004418974,0.00032181063,0.006267683,0.00035932756,0.0016102078,0.0002779936,0.00018283879,0.0023255055],"category_scores_gemma":[0.0057621365,0.00017102943,0.00053959276,0.011329334,0.00040560006,0.0009251134,0.0005481633,0.00032917058,0.0012097342],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029705153,0.00010834108,0.5755643,0.0006205302,0.00043886053,0.00042470437,0.00043451044,0.023416214,0.002119521,0.020923587,0.025789447,0.3498629],"study_design_scores_gemma":[0.000017329245,0.00011555206,0.7987877,0.000103390106,0.00014446983,0.0003639023,0.00036316074,0.003147434,0.0064254175,0.0024767455,0.18801945,0.00003536525],"about_ca_topic_score_codex":0.02196371,"about_ca_topic_score_gemma":0.017779283,"teacher_disagreement_score":0.02196371,"about_ca_system_score_codex":0.0023381081,"about_ca_system_score_gemma":0.0026956224,"threshold_uncertainty_score":0.043671727},"labels":[],"label_agreement":null},{"id":"W3140902533","doi":"","title":"비모수적 방법을 이용한 OECD 국가별 R&D 효율성과 생산성 분석","year":2003,"lang":"ko","type":"article","venue":"기술혁신연구","topic":"Engineering Applied Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Data envelopment analysis; Productivity; Econometrics; Economics; Technical change; Malmquist index; Technological change; Stock (firearms); Total factor productivity; Sample (material); Technical progress; Mathematics; Statistics; Macroeconomics; Geography","score_opus":0.010383459062494953,"score_gpt":0.23367476003202647,"score_spread":0.2232913009695315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3140902533","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7533418,0.012517114,0.012574538,0.0017910132,0.00018104122,0.000133782,0.045298457,0.0008244291,0.17333777],"genre_scores_gemma":[0.9090554,0.01235787,0.011767191,0.00037792724,0.00010655201,0.00019884907,0.034884702,0.0001434175,0.031108035],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978123,0.000216844,0.00015324849,0.00027572605,0.0012918579,0.0002499904],"domain_scores_gemma":[0.991338,0.0013797664,0.0025341471,0.00079123036,0.0037254775,0.0002313527],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0022448932,0.0004418974,0.00032181063,0.006267683,0.00035932756,0.0016102078,0.0002779936,0.00018283879,0.0023255055],"category_scores_gemma":[0.0057621365,0.00017102943,0.00053959276,0.011329334,0.00040560006,0.0009251134,0.0005481633,0.00032917058,0.0012097342],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029705153,0.00010834108,0.5755643,0.0006205302,0.00043886053,0.00042470437,0.00043451044,0.023416214,0.002119521,0.020923587,0.025789447,0.3498629],"study_design_scores_gemma":[0.000017329245,0.00011555206,0.7987877,0.000103390106,0.00014446983,0.0003639023,0.00036316074,0.003147434,0.0064254175,0.0024767455,0.18801945,0.00003536525],"about_ca_topic_score_codex":0.02196371,"about_ca_topic_score_gemma":0.017779283,"teacher_disagreement_score":0.9977551,"about_ca_system_score_codex":0.0023381081,"about_ca_system_score_gemma":0.0026956224,"threshold_uncertainty_score":0.043671727},"labels":[],"label_agreement":null},{"id":"W3157184388","doi":"","title":"The Comparison of Basic Science Research Capacity of OECD Countries","year":2003,"lang":"en","type":"article","venue":"기술혁신연구","topic":"Technology and Data Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Index (typography); Lag; Geography; Economics; Econometrics; Demographic economics","score_opus":0.08465945222868385,"score_gpt":0.3746106818337544,"score_spread":0.2899512296050706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157184388","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86459565,0.005752257,0.002598332,0.0010879531,0.00009261877,0.0000867554,0.00803754,0.0002142751,0.11753455],"genre_scores_gemma":[0.9900413,0.0024511032,0.0014735934,0.00012350436,0.000055821416,0.00008926517,0.003862807,0.000024367599,0.0018783173],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9963876,0.0007841238,0.00047850615,0.00031212103,0.0014134474,0.0006240873],"domain_scores_gemma":[0.9809576,0.003843555,0.005147767,0.0015704242,0.0071068425,0.0013737344],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.004789227,0.00036471125,0.00042420087,0.012167081,0.00043198137,0.0026240742,0.0003416727,0.00025971752,0.0021745027],"category_scores_gemma":[0.013857978,0.00016143863,0.00055628107,0.01155761,0.0007238959,0.0013947869,0.0020044032,0.00042318625,0.00055161177],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072572764,0.0001362141,0.76740307,0.0011184099,0.0009162839,0.000692036,0.0029890838,0.010889207,0.0035819132,0.02991953,0.008465068,0.17316344],"study_design_scores_gemma":[0.000024287292,0.00012494,0.9442811,0.00022486325,0.0001372276,0.00027130722,0.0024971587,0.0014006334,0.002486911,0.0026971665,0.045809753,0.00004463978],"about_ca_topic_score_codex":0.0067387763,"about_ca_topic_score_gemma":0.0035468608,"teacher_disagreement_score":0.99521077,"about_ca_system_score_codex":0.002207709,"about_ca_system_score_gemma":0.0025744888,"threshold_uncertainty_score":0.02532816},"labels":[],"label_agreement":null}]}