{"meta":{"query_hash":"56e4150d405c","filters":{"venue":"Proceedings of the 23rd ACM Conference on Economics and Computation"},"cohort_total":2,"direct_labels_cover":0,"predictions_cover":2,"exported":2,"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/56e4150d405c","api":"https://metacan.xera.ac/api/v1/cohort?venue=Proceedings+of+the+23rd+ACM+Conference+on+Economics+and+Computation"},"results":[{"id":"W4285090456","doi":"10.1145/3490486.3538343","title":"Peer Effects from Friends and Strangers: Evidence from Random Matchmaking in an Online Game","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 23rd ACM Conference on Economics and Computation","topic":"Experimental Behavioral Economics Studies","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Leverage (statistics); Peer effects; Product (mathematics); Peer-to-peer; Computer science; Peer influence; Psychology; Internet privacy; Business; Social psychology; World Wide Web; Mathematics; Artificial intelligence","score_opus":0.08598168647289438,"score_gpt":0.33528980747468595,"score_spread":0.24930812100179156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285090456","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9964975,0.0001719668,0.000004191659,0.0025299299,0.00016292089,0.00026756365,0.000055448472,0.000012715208,0.00029776408],"genre_scores_gemma":[0.9988424,0.00030031183,0.00064954825,0.00009956062,0.000044923247,0.000029978008,0.000009615245,0.000007800296,0.000015819944],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999213,0.00003104501,0.00022136864,0.0002940442,0.000108457585,0.00013207643],"domain_scores_gemma":[0.9993893,0.00020919536,0.00022401611,0.000062642,0.000069393354,0.000045434354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033187732,0.00010628762,0.00022068493,0.00005297306,0.0002650246,0.00016420482,0.00030928678,0.000035387908,0.00001598949],"category_scores_gemma":[0.000079266225,0.000105429724,0.000027778813,0.000058928712,0.0001540568,0.0004072336,0.0002937543,0.00013239653,4.174256e-7],"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":[0.0012950769,0.00063885306,0.51806307,0.000062034735,0.00013509138,0.0000017812808,0.2947201,0.0021058396,0.025215797,0.049963452,0.00009443076,0.10770448],"study_design_scores_gemma":[0.005426643,0.0009037666,0.42754495,0.0005058312,0.00013448764,0.0000014103747,0.16558477,0.079775155,0.005016129,0.31385887,0.0002720396,0.0009759614],"about_ca_topic_score_codex":0.0033924114,"about_ca_topic_score_gemma":0.0009770242,"teacher_disagreement_score":0.2638954,"about_ca_system_score_codex":0.00012210668,"about_ca_system_score_gemma":0.000039578943,"threshold_uncertainty_score":0.5128334},"labels":[],"label_agreement":null},{"id":"W4285090618","doi":"10.1145/3490486.3538329","title":"Optimal and Differentially Private Data Acquisition","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 23rd ACM Conference on Economics and Computation","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Differential privacy; Computer science; Minimax; Verifiable secret sharing; Estimator; Mechanism design; Information privacy; Private information retrieval; Payment; Mathematical optimization; Computer security; Data mining; Mathematics","score_opus":0.04934618704152341,"score_gpt":0.2629667816245659,"score_spread":0.21362059458304247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285090618","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9698162,0.000026442118,0.011234771,0.018267052,0.00015686697,0.00018241808,0.000051924908,0.0000775869,0.00018670209],"genre_scores_gemma":[0.9424467,0.00015482765,0.05722241,0.00012120508,0.000013100255,0.000012372803,0.00001722227,0.0000062368567,0.0000058951346],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990456,0.000010750983,0.00021510728,0.0004758986,0.00011221771,0.00014043409],"domain_scores_gemma":[0.99837416,0.000052486248,0.00029161837,0.0011964071,0.000055504144,0.000029837567],"candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0003143214,0.00011456705,0.00014618227,0.00008422923,0.0002617709,0.0002777379,0.0110487705,0.000034290097,0.000004022717],"category_scores_gemma":[0.0004339573,0.000103649334,0.000016725711,0.00011140401,0.00009476123,0.00063795794,0.08800736,0.0001642139,6.047595e-7],"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":[0.00012863585,0.00023679178,0.0052379635,0.00014197035,0.000118726064,8.1813596e-7,0.0005961283,0.0011169942,0.0073851193,0.6851578,0.009525489,0.2903536],"study_design_scores_gemma":[0.0002112028,0.0000959354,0.0048309783,0.000014116519,0.0000063702764,0.000010113127,0.00006462625,0.6873241,0.0008823523,0.30632553,0.00013137671,0.00010332193],"about_ca_topic_score_codex":0.0000074291547,"about_ca_topic_score_gemma":5.959513e-7,"teacher_disagreement_score":0.6862071,"about_ca_system_score_codex":0.00003428576,"about_ca_system_score_gemma":0.000031132477,"threshold_uncertainty_score":0.9943019},"labels":[],"label_agreement":null}]}