{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:UWHTLCHSOT4MSZCDP5U32XS4C3","short_pith_number":"pith:UWHTLCHS","schema_version":"1.0","canonical_sha256":"a58f3588f274f8c964437f69bd5e5c16c9015906386dbcedd3bd832a245e0e07","source":{"kind":"arxiv","id":"2606.21066","version":1},"attestation_state":"computed","paper":{"title":"Demographic Metadata as Construct-Irrelevant Noise in DistilBERT-Based Automated Essay Scoring","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hui Na Chua, Teik Peng Ch'ng","submitted_at":"2026-06-19T03:32:27Z","abstract_excerpt":"Automated Essay Scoring (AES) systems are increasingly used to support teachers in managing grading workloads and to provide a supplementary rater in large-scale assessments. While human grading is frequently influenced by students' demographic characteristics, the efficacy of different strategies for integrating demographic metadata with textual input used to train AES models remains underexplored. This study investigates the impact of a specific multimodal fusion strategy - naive metadata concatenation - on the predictive accuracy, training convergence, and score parity of a DistilBERT-based"},"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":"2606.21066","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-06-19T03:32:27Z","cross_cats_sorted":[],"title_canon_sha256":"1a04c906b99c99a1c0dda4ef41dbec518f9160a70b8ddc51a31b48477748636b","abstract_canon_sha256":"bc5ccdcb2d84e13059bdd4e75d64321b7eeb5fd252f546e12e934ceeef8d70af"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T01:12:28.740917Z","signature_b64":"QG+3WAPPqa3siz3mOQ9y8wpJGRD3OIlesnW4/gYkSnLaxXG26Yn0MMqFCKbSC4jR5bPbrLcd3thr9j8f86HoCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a58f3588f274f8c964437f69bd5e5c16c9015906386dbcedd3bd832a245e0e07","last_reissued_at":"2026-06-23T01:12:28.740417Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T01:12:28.740417Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Demographic Metadata as Construct-Irrelevant Noise in DistilBERT-Based Automated Essay Scoring","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hui Na Chua, Teik Peng Ch'ng","submitted_at":"2026-06-19T03:32:27Z","abstract_excerpt":"Automated Essay Scoring (AES) systems are increasingly used to support teachers in managing grading workloads and to provide a supplementary rater in large-scale assessments. While human grading is frequently influenced by students' demographic characteristics, the efficacy of different strategies for integrating demographic metadata with textual input used to train AES models remains underexplored. This study investigates the impact of a specific multimodal fusion strategy - naive metadata concatenation - on the predictive accuracy, training convergence, and score parity of a DistilBERT-based"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.21066","kind":"arxiv","version":1},"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/2606.21066/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":"2606.21066","created_at":"2026-06-23T01:12:28.740493+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.21066v1","created_at":"2026-06-23T01:12:28.740493+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.21066","created_at":"2026-06-23T01:12:28.740493+00:00"},{"alias_kind":"pith_short_12","alias_value":"UWHTLCHSOT4M","created_at":"2026-06-23T01:12:28.740493+00:00"},{"alias_kind":"pith_short_16","alias_value":"UWHTLCHSOT4MSZCD","created_at":"2026-06-23T01:12:28.740493+00:00"},{"alias_kind":"pith_short_8","alias_value":"UWHTLCHS","created_at":"2026-06-23T01:12:28.740493+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/UWHTLCHSOT4MSZCDP5U32XS4C3","json":"https://pith.science/pith/UWHTLCHSOT4MSZCDP5U32XS4C3.json","graph_json":"https://pith.science/api/pith-number/UWHTLCHSOT4MSZCDP5U32XS4C3/graph.json","events_json":"https://pith.science/api/pith-number/UWHTLCHSOT4MSZCDP5U32XS4C3/events.json","paper":"https://pith.science/paper/UWHTLCHS"},"agent_actions":{"view_html":"https://pith.science/pith/UWHTLCHSOT4MSZCDP5U32XS4C3","download_json":"https://pith.science/pith/UWHTLCHSOT4MSZCDP5U32XS4C3.json","view_paper":"https://pith.science/paper/UWHTLCHS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.21066&json=true","fetch_graph":"https://pith.science/api/pith-number/UWHTLCHSOT4MSZCDP5U32XS4C3/graph.json","fetch_events":"https://pith.science/api/pith-number/UWHTLCHSOT4MSZCDP5U32XS4C3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UWHTLCHSOT4MSZCDP5U32XS4C3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UWHTLCHSOT4MSZCDP5U32XS4C3/action/storage_attestation","attest_author":"https://pith.science/pith/UWHTLCHSOT4MSZCDP5U32XS4C3/action/author_attestation","sign_citation":"https://pith.science/pith/UWHTLCHSOT4MSZCDP5U32XS4C3/action/citation_signature","submit_replication":"https://pith.science/pith/UWHTLCHSOT4MSZCDP5U32XS4C3/action/replication_record"}},"created_at":"2026-06-23T01:12:28.740493+00:00","updated_at":"2026-06-23T01:12:28.740493+00:00"}