{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:V5Y35T2D7ZLJ5E3LCE6B42XDLL","short_pith_number":"pith:V5Y35T2D","schema_version":"1.0","canonical_sha256":"af71becf43fe569e936b113c1e6ae35ac68fd5a9c0f5d493e6f977a76dceda3e","source":{"kind":"arxiv","id":"2507.20358","version":1},"attestation_state":"computed","paper":{"title":"Beyond Binary Moderation: Identifying Fine-Grained Sexist and Misogynistic Behavior on GitHub with Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Amiangshu Bosu, Sayma Sultana, Tanni Dev","submitted_at":"2025-07-27T17:11:27Z","abstract_excerpt":"Background: Sexist and misogynistic behavior significantly hinders inclusion in technical communities like GitHub, causing developers, especially minorities, to leave due to subtle biases and microaggressions. Current moderation tools primarily rely on keyword filtering or binary classifiers, limiting their ability to detect nuanced harm effectively.\n  Aims: This study introduces a fine-grained, multi-class classification framework that leverages instruction-tuned Large Language Models (LLMs) to identify twelve distinct categories of sexist and misogynistic comments on GitHub.\n  Method: We uti"},"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":"2507.20358","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2025-07-27T17:11:27Z","cross_cats_sorted":[],"title_canon_sha256":"e15ab8ce2f72a29c9a42ed1390bd7ece604ee0d4083e768db8f50b4b6255a763","abstract_canon_sha256":"726b1af7dc2b5e14aefcb540080f57fee348f52fd17b6e6d867208d392f102f1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:44:17.744620Z","signature_b64":"IXq+R8gMBLUC1M076zjo7Zd0y2VgDGBE6wHWM2ucqTxt46KeKlJjjbFC3ixu978WNF9quoSz4VK6efMh2tHiCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af71becf43fe569e936b113c1e6ae35ac68fd5a9c0f5d493e6f977a76dceda3e","last_reissued_at":"2026-07-05T11:44:17.744130Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:44:17.744130Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond Binary Moderation: Identifying Fine-Grained Sexist and Misogynistic Behavior on GitHub with Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Amiangshu Bosu, Sayma Sultana, Tanni Dev","submitted_at":"2025-07-27T17:11:27Z","abstract_excerpt":"Background: Sexist and misogynistic behavior significantly hinders inclusion in technical communities like GitHub, causing developers, especially minorities, to leave due to subtle biases and microaggressions. Current moderation tools primarily rely on keyword filtering or binary classifiers, limiting their ability to detect nuanced harm effectively.\n  Aims: This study introduces a fine-grained, multi-class classification framework that leverages instruction-tuned Large Language Models (LLMs) to identify twelve distinct categories of sexist and misogynistic comments on GitHub.\n  Method: We uti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.20358","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/2507.20358/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":"2507.20358","created_at":"2026-07-05T11:44:17.744191+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.20358v1","created_at":"2026-07-05T11:44:17.744191+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.20358","created_at":"2026-07-05T11:44:17.744191+00:00"},{"alias_kind":"pith_short_12","alias_value":"V5Y35T2D7ZLJ","created_at":"2026-07-05T11:44:17.744191+00:00"},{"alias_kind":"pith_short_16","alias_value":"V5Y35T2D7ZLJ5E3L","created_at":"2026-07-05T11:44:17.744191+00:00"},{"alias_kind":"pith_short_8","alias_value":"V5Y35T2D","created_at":"2026-07-05T11:44:17.744191+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/V5Y35T2D7ZLJ5E3LCE6B42XDLL","json":"https://pith.science/pith/V5Y35T2D7ZLJ5E3LCE6B42XDLL.json","graph_json":"https://pith.science/api/pith-number/V5Y35T2D7ZLJ5E3LCE6B42XDLL/graph.json","events_json":"https://pith.science/api/pith-number/V5Y35T2D7ZLJ5E3LCE6B42XDLL/events.json","paper":"https://pith.science/paper/V5Y35T2D"},"agent_actions":{"view_html":"https://pith.science/pith/V5Y35T2D7ZLJ5E3LCE6B42XDLL","download_json":"https://pith.science/pith/V5Y35T2D7ZLJ5E3LCE6B42XDLL.json","view_paper":"https://pith.science/paper/V5Y35T2D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.20358&json=true","fetch_graph":"https://pith.science/api/pith-number/V5Y35T2D7ZLJ5E3LCE6B42XDLL/graph.json","fetch_events":"https://pith.science/api/pith-number/V5Y35T2D7ZLJ5E3LCE6B42XDLL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V5Y35T2D7ZLJ5E3LCE6B42XDLL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V5Y35T2D7ZLJ5E3LCE6B42XDLL/action/storage_attestation","attest_author":"https://pith.science/pith/V5Y35T2D7ZLJ5E3LCE6B42XDLL/action/author_attestation","sign_citation":"https://pith.science/pith/V5Y35T2D7ZLJ5E3LCE6B42XDLL/action/citation_signature","submit_replication":"https://pith.science/pith/V5Y35T2D7ZLJ5E3LCE6B42XDLL/action/replication_record"}},"created_at":"2026-07-05T11:44:17.744191+00:00","updated_at":"2026-07-05T11:44:17.744191+00:00"}