{"meta":{"query_hash":"36750f61a174","filters":{"venue":"The Review of Socionetwork Strategies"},"cohort_total":6,"direct_labels_cover":0,"predictions_cover":6,"exported":6,"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/36750f61a174","api":"https://metacan.xera.ac/api/v1/cohort?venue=The+Review+of+Socionetwork+Strategies"},"results":[{"id":"W4210931461","doi":"10.1007/s12626-022-00103-1","title":"Legal Information Retrieval and Entailment Based on BM25, Transformer and Semantic Thesaurus Methods","year":2022,"lang":"en","type":"article","venue":"The Review of Socionetwork Strategies","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Textual entailment; Natural language processing; Logical consequence; Information retrieval; Artificial intelligence; Thesaurus; Information extraction; Question answering; Natural language; Task (project management); Semantic computing; Semantic Web","score_opus":0.03401550290011908,"score_gpt":0.38594936615372927,"score_spread":0.3519338632536102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210931461","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12830895,0.34556866,0.04739275,0.14306226,0.0032510348,0.012120883,0.00011221872,0.000486242,0.319697],"genre_scores_gemma":[0.93956935,0.058251344,0.00069314044,0.0012958392,0.000089518006,0.00004164933,0.0000069473667,0.0000067081005,0.0000454744],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99790907,0.0009812082,0.00036492324,0.000104407954,0.00044590022,0.00019446542],"domain_scores_gemma":[0.9990301,0.00054493966,0.0001868037,0.00013834462,0.000058648748,0.00004112258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044385907,0.00010345135,0.00022956824,0.000029085964,0.001001988,0.00009167562,0.00019133142,0.000034899516,0.00027810686],"category_scores_gemma":[0.000088569344,0.00007409505,0.00007301612,0.00028635602,0.00058869837,0.00043356715,0.000030455663,0.00021165478,0.0000034517918],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001411965,0.00005496224,0.000083776984,0.0015047013,0.000052001313,0.0000010153445,0.023940535,0.001350887,0.00003694364,0.7988204,0.0011440617,0.17286952],"study_design_scores_gemma":[0.0002986415,0.0006891431,0.00057806115,0.0027755992,0.00035876594,0.000006854181,0.15807697,0.0047800983,0.00032637815,0.06036231,0.7711487,0.0005985087],"about_ca_topic_score_codex":0.0003601315,"about_ca_topic_score_gemma":0.000033138662,"teacher_disagreement_score":0.8112604,"about_ca_system_score_codex":0.00006467579,"about_ca_system_score_gemma":0.0001845969,"threshold_uncertainty_score":0.77065825},"labels":[],"label_agreement":null},{"id":"W4213191780","doi":"10.1007/s12626-022-00105-z","title":"Overview and Discussion of the Competition on Legal Information Extraction/Entailment (COLIEE) 2021","year":2022,"lang":"en","type":"article","venue":"The Review of Socionetwork Strategies","topic":"Topic Modeling","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Hokkaido University; Shizuoka University; University of Alberta; National Institute of Informatics; Alberta Machine Intelligence Institute","keywords":"Task (project management); Statute; Computer science; Logical consequence; Component (thermodynamics); Competition (biology); Natural language processing; Information retrieval; Law; Artificial intelligence; Political science","score_opus":0.020410539368644436,"score_gpt":0.28133437493635527,"score_spread":0.2609238355677108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213191780","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061740216,0.4450953,0.25962248,0.19488788,0.0056939987,0.0073179896,0.00006625109,0.0002114719,0.02536439],"genre_scores_gemma":[0.9331319,0.064935364,0.00086272764,0.00088422507,0.000060035673,0.00006297807,0.00000740963,0.000003789139,0.00005156596],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988325,0.00027045913,0.00034406612,0.0000900637,0.000375293,0.00008762262],"domain_scores_gemma":[0.999168,0.000060041435,0.00035408125,0.00036518576,0.00003925866,0.0000134247175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072392164,0.000077077384,0.00016209522,0.000016943348,0.0002670966,0.000042906522,0.00039309007,0.000014407612,0.000054538057],"category_scores_gemma":[0.0000078507865,0.00003749966,0.00008103759,0.00021631256,0.00005451104,0.00052578613,0.00027704382,0.00016643897,0.0000019553825],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004958095,0.000038963313,0.00003346818,0.0015676823,0.000018282957,1.8568542e-7,0.0009143458,0.008255433,0.000023466293,0.9190121,0.0010844738,0.06904663],"study_design_scores_gemma":[0.0020275437,0.0010484006,0.025922198,0.042729124,0.00041908657,0.00014725227,0.02064966,0.14693135,0.00037639894,0.16381401,0.5946102,0.0013247428],"about_ca_topic_score_codex":0.000024070323,"about_ca_topic_score_gemma":0.000001915572,"teacher_disagreement_score":0.8713917,"about_ca_system_score_codex":0.0000465981,"about_ca_system_score_gemma":0.00007397825,"threshold_uncertainty_score":0.2054318},"labels":[],"label_agreement":null},{"id":"W4390745240","doi":"10.1007/s12626-023-00153-z","title":"Legal Information Retrieval and Entailment Using Transformer-based Approaches","year":2024,"lang":"en","type":"article","venue":"The Review of Socionetwork Strategies","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; University of Alberta; Alberta Machine Intelligence Institute","keywords":"Computer science; Transformer; Textual entailment; Information retrieval; Logical consequence; Natural language processing; Artificial intelligence; Physics; Voltage; Quantum mechanics","score_opus":0.09925702547852286,"score_gpt":0.36733460609135166,"score_spread":0.2680775806128288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390745240","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07516387,0.7024443,0.05882622,0.037409607,0.0019804817,0.004268948,0.00003664485,0.0004671719,0.119402744],"genre_scores_gemma":[0.9437541,0.055450942,0.00034813234,0.00019140258,0.00021323859,0.000008969947,0.00000654109,0.000006069549,0.000020611791],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987949,0.00020220192,0.0003696781,0.0000913727,0.0003499451,0.00019186226],"domain_scores_gemma":[0.9995134,0.00020335976,0.00009178427,0.00010053196,0.000054488744,0.00003643178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001870205,0.000097302196,0.00018030158,0.000026848838,0.0003976907,0.00022634046,0.00015954683,0.000060391547,0.00008610063],"category_scores_gemma":[0.00003595758,0.00006632576,0.000098675,0.00035407586,0.0007567604,0.0009800509,0.000010343332,0.000145191,0.000011643978],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017528011,0.000012837518,0.000012885359,0.0037020477,0.000040892664,6.835574e-7,0.0108400555,0.0007436082,0.00002045255,0.9328545,0.0004462884,0.051308222],"study_design_scores_gemma":[0.00015008495,0.00020742773,0.000079956015,0.028053004,0.00070957805,0.000008400235,0.14632581,0.027955646,0.0009972313,0.06740158,0.72729284,0.0008184399],"about_ca_topic_score_codex":0.00041450473,"about_ca_topic_score_gemma":0.000052105883,"teacher_disagreement_score":0.86859024,"about_ca_system_score_codex":0.00007404986,"about_ca_system_score_gemma":0.00042544166,"threshold_uncertainty_score":0.30587554},"labels":[],"label_agreement":null},{"id":"W4390822940","doi":"10.1007/s12626-023-00152-0","title":"Overview and Discussion of the Competition on Legal Information, Extraction/Entailment (COLIEE) 2023","year":2024,"lang":"en","type":"article","venue":"The Review of Socionetwork Strategies","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Institute of Informatics; Shizuoka University; Alberta Innovates; Alberta Machine Intelligence Institute; Hokkaido University; University of Alberta","keywords":"Task (project management); Statute; Computer science; Logical consequence; Competition (biology); Component (thermodynamics); Common law; Task group; Information retrieval; Natural language processing; Law; Political science; Artificial intelligence; Economics; Engineering","score_opus":0.04004910666469563,"score_gpt":0.3822646249559034,"score_spread":0.3422155182912078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390822940","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023612805,0.54442275,0.002056871,0.20684417,0.005949974,0.0052843885,0.000082877,0.00025061224,0.21149558],"genre_scores_gemma":[0.72081184,0.27827772,0.00003052806,0.00036623728,0.00018656967,0.000026544061,0.000004967568,0.0000047258595,0.00029087786],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99866617,0.00031190732,0.00040922183,0.000079917234,0.00040869132,0.00012408655],"domain_scores_gemma":[0.99929434,0.00022524163,0.00020239926,0.00016671997,0.00008757675,0.000023712582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012822448,0.000085056636,0.00016932764,0.000018061975,0.00042180312,0.000104768245,0.00020766584,0.000046041867,0.000252504],"category_scores_gemma":[0.000055856442,0.000038747974,0.00011371498,0.00034894148,0.00062124064,0.0006576054,0.000043831493,0.00015698493,0.00003650071],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034112375,0.000013185193,0.00001663557,0.0015650681,0.00001714977,1.2001084e-7,0.0025762073,0.0001136174,0.0000073613364,0.9711849,0.0046622627,0.019840086],"study_design_scores_gemma":[0.00003460795,0.000064922984,0.0010415093,0.02732476,0.00012828613,0.0000021057435,0.026589341,0.00022532727,0.00009559196,0.0732296,0.87110335,0.00016057375],"about_ca_topic_score_codex":0.00041938422,"about_ca_topic_score_gemma":0.00019867884,"teacher_disagreement_score":0.8979553,"about_ca_system_score_codex":0.000059138925,"about_ca_system_score_gemma":0.00017540091,"threshold_uncertainty_score":0.3244211},"labels":[],"label_agreement":null},{"id":"W4392926280","doi":"10.1007/s12626-024-00159-1","title":"Preface of Special Issue on 10th Competition on Legal Information of Extraction and Entailment (COLIEE 2023)","year":2024,"lang":"en","type":"article","venue":"The Review of Socionetwork Strategies","topic":"European and International Contract Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Logical consequence; Competition (biology); Extraction (chemistry); Political science; Epistemology; Philosophy; Chemistry; Chromatography; Biology","score_opus":0.018054464331107307,"score_gpt":0.33307275652991836,"score_spread":0.31501829219881106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392926280","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03359602,0.027438238,0.00012967484,0.005658161,0.0013119037,0.0009857228,0.000046116453,0.000039930404,0.93079424],"genre_scores_gemma":[0.861576,0.13627174,0.000025167701,0.00020110255,0.0013369935,0.0000063722255,0.000016377006,0.0000043352243,0.0005618601],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989794,0.00018684506,0.00033594237,0.000064651336,0.00035644884,0.000076722376],"domain_scores_gemma":[0.9994037,0.00018316577,0.00022201972,0.00007526214,0.00009805758,0.000017786231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008132661,0.00006950889,0.000160201,0.000032811506,0.00008985576,0.000050455077,0.00010447905,0.000030361718,0.00038637416],"category_scores_gemma":[0.00003640101,0.000049024384,0.00007049799,0.00012084546,0.00020685751,0.00050001795,0.000012684943,0.0001133047,0.000037048965],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026843427,0.00002678146,0.0000033601705,0.0010854778,0.000028795017,2.6029207e-7,0.0012458165,0.000114540926,0.000028409171,0.9799681,0.0047154683,0.012756183],"study_design_scores_gemma":[0.00010088306,0.00022584287,0.00100229,0.008474008,0.000050736264,5.7847063e-7,0.0028621512,0.00007563957,0.00014955348,0.0026249534,0.98435444,0.00007894893],"about_ca_topic_score_codex":0.000097316166,"about_ca_topic_score_gemma":0.00002516493,"teacher_disagreement_score":0.97963893,"about_ca_system_score_codex":0.000044698914,"about_ca_system_score_gemma":0.000081800135,"threshold_uncertainty_score":0.4230526},"labels":[],"label_agreement":null},{"id":"W4408404259","doi":"10.1007/s12626-025-00181-x","title":"An Advanced Deep Learning Framework for Skin Cancer Classification","year":2025,"lang":"en","type":"article","venue":"The Review of Socionetwork Strategies","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Cancer; Computer science; Skin cancer; Deep learning; Artificial intelligence; Medicine","score_opus":0.02182250615061434,"score_gpt":0.3658657292189873,"score_spread":0.344043223068373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408404259","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018444378,0.60110223,0.3323373,0.023210444,0.0012861931,0.005384904,0.000005249768,0.00034474768,0.01788459],"genre_scores_gemma":[0.7062152,0.28720576,0.0038567907,0.0013131101,0.0002498364,0.00039190962,0.000023210328,0.000014879261,0.0007293051],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99925786,0.00008022668,0.00027082715,0.00014749325,0.000111712325,0.00013187173],"domain_scores_gemma":[0.9992736,0.00015227128,0.00017284289,0.0002556253,0.00012029869,0.000025338208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036266874,0.00009841238,0.0002823092,0.000028886227,0.00016139096,0.000017021146,0.00010064574,0.00005599942,0.00008043408],"category_scores_gemma":[0.00006028025,0.00006726396,0.0001233399,0.00024108583,0.00006977152,0.000050332736,0.000013750001,0.0001791938,0.0000031526035],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013980614,0.00007273598,0.0001356715,0.010431353,0.00020459728,7.2354914e-7,0.0003910762,0.0011807623,0.00031630573,0.24210718,0.0013797644,0.74364],"study_design_scores_gemma":[0.00189272,0.0011686492,0.017166995,0.068465285,0.002818951,0.000020653719,0.03311032,0.015845787,0.00047086627,0.091811314,0.7665634,0.00066503184],"about_ca_topic_score_codex":0.000008393866,"about_ca_topic_score_gemma":0.000009196391,"teacher_disagreement_score":0.7651837,"about_ca_system_score_codex":0.000056195295,"about_ca_system_score_gemma":0.000073131516,"threshold_uncertainty_score":0.27429447},"labels":[],"label_agreement":null}]}