{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:BDZSHWSWCVDKIFCUDJT2ZX2NEG","short_pith_number":"pith:BDZSHWSW","schema_version":"1.0","canonical_sha256":"08f323da561546a414541a67acdf4d21a13ea097b549d8c6d1f784089b81a01d","source":{"kind":"arxiv","id":"2009.10277","version":2},"attestation_state":"computed","paper":{"title":"Measuring a hate speech spectrum with faceted Rasch item response theory and perspective-aware, explainable-by-design deep learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SI"],"primary_cat":"cs.CL","authors_text":"Alexander Sahn, Chris J. Kennedy, Claudia von Vacano, Geoff Bacon","submitted_at":"2020-09-22T02:15:05Z","abstract_excerpt":"We propose a system for measuring hate speech on a continuous, interval-valued spectrum ranging from genocidal to supportive speech by combining supervised deep learning with faceted Rasch item response theory (IRT). We decompose the theoretical construct of hate speech into constituent concepts operationalized as 10 ordinal labels. Those labels are reconstituted via IRT probabilistic latent modeling into an interval outcome measure while simultaneously estimating and adjusting for each annotator's labeling perspective. Our scaling procedure integrates naturally with a multitask deep learning "},"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":"2009.10277","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-09-22T02:15:05Z","cross_cats_sorted":["cs.LG","cs.SI"],"title_canon_sha256":"8e782e13d8e815d720d5bb65ff167b3f4f79904161c083d8fed355e8324cd7ff","abstract_canon_sha256":"a3c5a1aa268106e5b92c706d472af07fb091abe1a6a71bd0c1d932a43a016993"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T02:08:24.432236Z","signature_b64":"5lwnulxl8cFrUdI6uo8S3CnxRTegpTMjO8Zhjc6IvLCODxvlddQ7xwPfSl5/XHu0oPE+qRDjJOInPyMLlpp3Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"08f323da561546a414541a67acdf4d21a13ea097b549d8c6d1f784089b81a01d","last_reissued_at":"2026-06-09T02:08:24.431351Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T02:08:24.431351Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Measuring a hate speech spectrum with faceted Rasch item response theory and perspective-aware, explainable-by-design deep learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SI"],"primary_cat":"cs.CL","authors_text":"Alexander Sahn, Chris J. Kennedy, Claudia von Vacano, Geoff Bacon","submitted_at":"2020-09-22T02:15:05Z","abstract_excerpt":"We propose a system for measuring hate speech on a continuous, interval-valued spectrum ranging from genocidal to supportive speech by combining supervised deep learning with faceted Rasch item response theory (IRT). We decompose the theoretical construct of hate speech into constituent concepts operationalized as 10 ordinal labels. Those labels are reconstituted via IRT probabilistic latent modeling into an interval outcome measure while simultaneously estimating and adjusting for each annotator's labeling perspective. Our scaling procedure integrates naturally with a multitask deep learning "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.10277","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/2009.10277/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":"2009.10277","created_at":"2026-06-09T02:08:24.431512+00:00"},{"alias_kind":"arxiv_version","alias_value":"2009.10277v2","created_at":"2026-06-09T02:08:24.431512+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.10277","created_at":"2026-06-09T02:08:24.431512+00:00"},{"alias_kind":"pith_short_12","alias_value":"BDZSHWSWCVDK","created_at":"2026-06-09T02:08:24.431512+00:00"},{"alias_kind":"pith_short_16","alias_value":"BDZSHWSWCVDKIFCU","created_at":"2026-06-09T02:08:24.431512+00:00"},{"alias_kind":"pith_short_8","alias_value":"BDZSHWSW","created_at":"2026-06-09T02:08:24.431512+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":4,"sample":[{"citing_arxiv_id":"2507.05660","citing_title":"Optimus: A Robust Defense Framework for Mitigating Toxicity while Fine-Tuning Conversational AI","ref_index":37,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11954","citing_title":"Assessing and Mitigating Miscalibration in LLM-Based Social Science Measurement","ref_index":70,"is_internal_anchor":true},{"citing_arxiv_id":"2605.01168","citing_title":"Quantifying and Predicting Disagreement in Graded Human Ratings","ref_index":204,"is_internal_anchor":true},{"citing_arxiv_id":"2604.18069","citing_title":"Modeling Human Perspectives with Socio-Demographic Representations","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BDZSHWSWCVDKIFCUDJT2ZX2NEG","json":"https://pith.science/pith/BDZSHWSWCVDKIFCUDJT2ZX2NEG.json","graph_json":"https://pith.science/api/pith-number/BDZSHWSWCVDKIFCUDJT2ZX2NEG/graph.json","events_json":"https://pith.science/api/pith-number/BDZSHWSWCVDKIFCUDJT2ZX2NEG/events.json","paper":"https://pith.science/paper/BDZSHWSW"},"agent_actions":{"view_html":"https://pith.science/pith/BDZSHWSWCVDKIFCUDJT2ZX2NEG","download_json":"https://pith.science/pith/BDZSHWSWCVDKIFCUDJT2ZX2NEG.json","view_paper":"https://pith.science/paper/BDZSHWSW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2009.10277&json=true","fetch_graph":"https://pith.science/api/pith-number/BDZSHWSWCVDKIFCUDJT2ZX2NEG/graph.json","fetch_events":"https://pith.science/api/pith-number/BDZSHWSWCVDKIFCUDJT2ZX2NEG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BDZSHWSWCVDKIFCUDJT2ZX2NEG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BDZSHWSWCVDKIFCUDJT2ZX2NEG/action/storage_attestation","attest_author":"https://pith.science/pith/BDZSHWSWCVDKIFCUDJT2ZX2NEG/action/author_attestation","sign_citation":"https://pith.science/pith/BDZSHWSWCVDKIFCUDJT2ZX2NEG/action/citation_signature","submit_replication":"https://pith.science/pith/BDZSHWSWCVDKIFCUDJT2ZX2NEG/action/replication_record"}},"created_at":"2026-06-09T02:08:24.431512+00:00","updated_at":"2026-06-09T02:08:24.431512+00:00"}