{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:WXMU5L43JOHLM3MGNYGJXZPOBK","short_pith_number":"pith:WXMU5L43","schema_version":"1.0","canonical_sha256":"b5d94eaf9b4b8eb66d866e0c9be5ee0a912884a1ad7ac2f3bd7bbae10559dd6e","source":{"kind":"arxiv","id":"2209.01242","version":2},"attestation_state":"computed","paper":{"title":"Better Peer Grading through Bayesian Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GT"],"primary_cat":"cs.AI","authors_text":"Greg d'Eon, Hedayat Zarkoob, Kevin Leyton-Brown, Lena Podina","submitted_at":"2022-09-02T19:10:53Z","abstract_excerpt":"Peer grading systems aggregate noisy reports from multiple students to approximate a true grade as closely as possible. Most current systems either take the mean or median of reported grades; others aim to estimate students' grading accuracy under a probabilistic model. This paper extends the state of the art in the latter approach in three key ways: (1) recognizing that students can behave strategically (e.g., reporting grades close to the class average without doing the work); (2) appropriately handling censored data that arises from discrete-valued grading rubrics; and (3) using mixed integ"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2209.01242","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2022-09-02T19:10:53Z","cross_cats_sorted":["cs.GT"],"title_canon_sha256":"cd7f6877b373038ff736d02108e16b88458dc60f72a02642679675652732a255","abstract_canon_sha256":"89b971f1cbf3f8a851e40061ed21ecf4d51fe2f92a4b192fe95c410e75b8c93e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:21:43.327684Z","signature_b64":"bciGdga+5AQYNNXw4vkQO88QZms3Enj/ACX+VGx92q2AMHTuBpR67sJCfwfIMq9L3zBOxtl9YEH51Dbso4VNBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b5d94eaf9b4b8eb66d866e0c9be5ee0a912884a1ad7ac2f3bd7bbae10559dd6e","last_reissued_at":"2026-07-05T05:21:43.327264Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:21:43.327264Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Better Peer Grading through Bayesian Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GT"],"primary_cat":"cs.AI","authors_text":"Greg d'Eon, Hedayat Zarkoob, Kevin Leyton-Brown, Lena Podina","submitted_at":"2022-09-02T19:10:53Z","abstract_excerpt":"Peer grading systems aggregate noisy reports from multiple students to approximate a true grade as closely as possible. Most current systems either take the mean or median of reported grades; others aim to estimate students' grading accuracy under a probabilistic model. This paper extends the state of the art in the latter approach in three key ways: (1) recognizing that students can behave strategically (e.g., reporting grades close to the class average without doing the work); (2) appropriately handling censored data that arises from discrete-valued grading rubrics; and (3) using mixed integ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.01242","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2209.01242/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2209.01242","created_at":"2026-07-05T05:21:43.327344+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.01242v2","created_at":"2026-07-05T05:21:43.327344+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.01242","created_at":"2026-07-05T05:21:43.327344+00:00"},{"alias_kind":"pith_short_12","alias_value":"WXMU5L43JOHL","created_at":"2026-07-05T05:21:43.327344+00:00"},{"alias_kind":"pith_short_16","alias_value":"WXMU5L43JOHLM3MG","created_at":"2026-07-05T05:21:43.327344+00:00"},{"alias_kind":"pith_short_8","alias_value":"WXMU5L43","created_at":"2026-07-05T05:21:43.327344+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WXMU5L43JOHLM3MGNYGJXZPOBK","json":"https://pith.science/pith/WXMU5L43JOHLM3MGNYGJXZPOBK.json","graph_json":"https://pith.science/api/pith-number/WXMU5L43JOHLM3MGNYGJXZPOBK/graph.json","events_json":"https://pith.science/api/pith-number/WXMU5L43JOHLM3MGNYGJXZPOBK/events.json","paper":"https://pith.science/paper/WXMU5L43"},"agent_actions":{"view_html":"https://pith.science/pith/WXMU5L43JOHLM3MGNYGJXZPOBK","download_json":"https://pith.science/pith/WXMU5L43JOHLM3MGNYGJXZPOBK.json","view_paper":"https://pith.science/paper/WXMU5L43","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.01242&json=true","fetch_graph":"https://pith.science/api/pith-number/WXMU5L43JOHLM3MGNYGJXZPOBK/graph.json","fetch_events":"https://pith.science/api/pith-number/WXMU5L43JOHLM3MGNYGJXZPOBK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WXMU5L43JOHLM3MGNYGJXZPOBK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WXMU5L43JOHLM3MGNYGJXZPOBK/action/storage_attestation","attest_author":"https://pith.science/pith/WXMU5L43JOHLM3MGNYGJXZPOBK/action/author_attestation","sign_citation":"https://pith.science/pith/WXMU5L43JOHLM3MGNYGJXZPOBK/action/citation_signature","submit_replication":"https://pith.science/pith/WXMU5L43JOHLM3MGNYGJXZPOBK/action/replication_record"}},"created_at":"2026-07-05T05:21:43.327344+00:00","updated_at":"2026-07-05T05:21:43.327344+00:00"}