{"meta":{"query_hash":"b777789ddbc2","filters":{"venue":"International Journal of Information Quality"},"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/b777789ddbc2","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Journal+of+Information+Quality"},"results":[{"id":"W1999393802","doi":"10.1504/ijiq.2008.022958","title":"The National Ambulatory Care Reporting System: factors that affect the quality of its emergency data","year":2008,"lang":"en","type":"article","venue":"International Journal of Information Quality","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Institute for Health Information","funders":"","keywords":"Data collection; Medical emergency; Data quality; Emergency department; Affect (linguistics); Quality (philosophy); Activity-based costing; Ambulatory care; Operations management; Medicine; Computer science; Business; Health care; Nursing; Psychology; Engineering; Statistics","score_opus":0.3943013979723309,"score_gpt":0.4347433494879706,"score_spread":0.04044195151563973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999393802","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9291427,0.005337612,0.010697757,0.024399847,0.00014391459,0.0008611684,0.009297763,0.00019318344,0.019926082],"genre_scores_gemma":[0.99256516,0.00056007743,0.0030799354,0.0006645178,0.00011344099,0.00010560923,0.002371662,0.000031061052,0.00050846447],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.87760603,0.050988782,0.014692936,0.0040904875,0.049290292,0.003331442],"domain_scores_gemma":[0.48893687,0.27751154,0.13979906,0.017908549,0.07075581,0.005088089],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.090777315,0.000268339,0.00052391674,0.0044827526,0.0014892905,0.0034134374,0.0018323062,0.00071517617,0.0013116179],"category_scores_gemma":[0.3480369,0.0004935499,0.00044423086,0.015580122,0.001900541,0.0024022565,0.0018740811,0.0008961099,0.00030954272],"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":[0.00008966757,0.00003058383,0.9805829,0.0001264542,0.000067506255,0.00003742973,0.0018567853,0.00038578472,0.00012947433,0.00057328306,0.0021953583,0.01392478],"study_design_scores_gemma":[0.00001037025,0.000046834986,0.9943376,0.000096305484,0.000026310458,0.000078501114,0.0010575282,0.0011388774,0.00015626171,0.00017918073,0.002852911,0.000019299321],"about_ca_topic_score_codex":0.3792298,"about_ca_topic_score_gemma":0.27651855,"teacher_disagreement_score":0.90922266,"about_ca_system_score_codex":0.010049959,"about_ca_system_score_gemma":0.009732955,"threshold_uncertainty_score":0.7540449},"labels":[],"label_agreement":null},{"id":"W2083947418","doi":"10.1504/ijiq.2014.068653","title":"Repairing integrity rules for improved data quality","year":2014,"lang":"en","type":"article","venue":"International Journal of Information Quality","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Data integrity; Constraint (computer-aided design); Data quality; Data mining; Data cleansing; Quality (philosophy); Set (abstract data type); Business rule; Domain (mathematical analysis); Data science; Business process; Database; Work in process","score_opus":0.37274437675812616,"score_gpt":0.5310786126391739,"score_spread":0.15833423588104772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083947418","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034287028,0.00088366086,0.95241094,0.0023287626,0.0001654181,0.0007506402,0.0012457586,0.0056988667,0.0022289795],"genre_scores_gemma":[0.09732234,0.00038991743,0.89805466,0.00041384163,0.00007213607,0.00019536026,0.002048191,0.0006703592,0.00083330186],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9556271,0.012067119,0.0076011117,0.005002197,0.018275257,0.0014273883],"domain_scores_gemma":[0.76429087,0.08962894,0.030446876,0.08166741,0.032114536,0.0018514015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.036266625,0.0016245975,0.0022696157,0.007318707,0.0025570807,0.007281277,0.0053710267,0.0024122165,0.0031075603],"category_scores_gemma":[0.19351073,0.0014719323,0.0028721837,0.0076403217,0.0027248843,0.010225479,0.007057748,0.0046556494,0.0011851094],"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":[0.0004806851,0.0006789713,0.042080063,0.0021898493,0.00069530663,0.001661345,0.005999249,0.10630976,0.028788222,0.04676764,0.02040799,0.7439409],"study_design_scores_gemma":[0.0002282833,0.00054970675,0.0127915405,0.0012458719,0.00079863344,0.003041333,0.0035616904,0.62060744,0.121577375,0.1291089,0.10606794,0.00042124654],"about_ca_topic_score_codex":0.008691045,"about_ca_topic_score_gemma":0.009836439,"teacher_disagreement_score":0.036266625,"about_ca_system_score_codex":0.0020949864,"about_ca_system_score_gemma":0.008291084,"threshold_uncertainty_score":0.19179851},"labels":[],"label_agreement":null},{"id":"W2169710203","doi":"10.1504/ijiq.2008.019560","title":"Information quality chain analysis for total information quality management","year":2008,"lang":"en","type":"article","venue":"International Journal of Information Quality","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Information quality; Quality (philosophy); Data quality; Information management; Knowledge management; Information system; Information retrieval; Business","score_opus":0.1660276901368132,"score_gpt":0.459138105283643,"score_spread":0.2931104151468298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169710203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006050219,0.00085039024,0.97272193,0.0005614727,0.0000764591,0.00025649174,0.00027560093,0.00020226488,0.019005194],"genre_scores_gemma":[0.2509207,0.0018352239,0.7390687,0.00027681168,0.00016810432,0.00088665937,0.0008453337,0.0001547262,0.0058437013],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99303997,0.0032457605,0.00045686233,0.00053581304,0.0024568655,0.00026474695],"domain_scores_gemma":[0.9894215,0.0057465783,0.0011092512,0.001284117,0.0022611187,0.00017747968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067658406,0.0009622813,0.0006530661,0.0072540944,0.0014802819,0.0041524777,0.0010808078,0.0008880109,0.0149671],"category_scores_gemma":[0.018733207,0.0003231853,0.0015397421,0.012427238,0.0019703112,0.0058958516,0.002196284,0.0017930553,0.0014736293],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041037885,0.000058503858,0.0031205185,0.0003505003,0.00009080797,0.00008582362,0.00048327202,0.032284863,0.00064762164,0.82836705,0.0033742893,0.13109584],"study_design_scores_gemma":[0.000016363534,0.00008721681,0.002265194,0.00028446814,0.000086734726,0.000090711204,0.0006108731,0.2065676,0.0016799236,0.75885797,0.02939962,0.00005325694],"about_ca_topic_score_codex":0.0073795263,"about_ca_topic_score_gemma":0.0038485187,"teacher_disagreement_score":0.0149671,"about_ca_system_score_codex":0.0038978874,"about_ca_system_score_gemma":0.004512555,"threshold_uncertainty_score":0.050069988},"labels":[],"label_agreement":null}]}