{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FJXTE6CGW2IKGFS4FZ5OMU42IE","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e08d91ad3a477e929541fed606bbf8b03f06c2474ca1f173321b3b5e0dd1e2d7","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2025-03-08T04:06:16Z","title_canon_sha256":"6f702c017e32551237465a7256119bb10d030c9a4b6f6a332f546d44de179d19"},"schema_version":"1.0","source":{"id":"2503.16485","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.16485","created_at":"2026-07-05T10:37:48Z"},{"alias_kind":"arxiv_version","alias_value":"2503.16485v2","created_at":"2026-07-05T10:37:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.16485","created_at":"2026-07-05T10:37:48Z"},{"alias_kind":"pith_short_12","alias_value":"FJXTE6CGW2IK","created_at":"2026-07-05T10:37:48Z"},{"alias_kind":"pith_short_16","alias_value":"FJXTE6CGW2IKGFS4","created_at":"2026-07-05T10:37:48Z"},{"alias_kind":"pith_short_8","alias_value":"FJXTE6CG","created_at":"2026-07-05T10:37:48Z"}],"graph_snapshots":[{"event_id":"sha256:f28ba8be7c9bd19afa322bbe82a08cb39673dec07ff4481c38dd7a64737f9b97","target":"graph","created_at":"2026-07-05T10:37:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2503.16485/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This study highlights the transparency and accuracy of GenAI's inductive thematic analysis, particularly using GPT-4 Turbo API integrated within a stepwise prompt-based Python script. This approach ensured a traceable and systematic coding process, generating codes with supporting statements and page references, which enhanced validation and reproducibility. The results indicate that GenAI performs inductive coding in a manner closely resembling human coders, effectively categorizing themes at a level like the average human coder. However, in interpretation, GenAI extends beyond human coders b","authors_text":"Emmanuel Dwamena, Kwame Owoahene Acheampong, Mary Abiswin Apam, Matthew Nyaaba, Min SungEun","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2025-03-08T04:06:16Z","title":"Optimizing Generative AI's Accuracy and Transparency in Inductive Thematic Analysis: A Human-AI Comparison"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.16485","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e8552c5c1b3f0efed10472d7dc0fa5a3f8b394decc09963df7b79b79b15194dc","target":"record","created_at":"2026-07-05T10:37:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e08d91ad3a477e929541fed606bbf8b03f06c2474ca1f173321b3b5e0dd1e2d7","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2025-03-08T04:06:16Z","title_canon_sha256":"6f702c017e32551237465a7256119bb10d030c9a4b6f6a332f546d44de179d19"},"schema_version":"1.0","source":{"id":"2503.16485","kind":"arxiv","version":2}},"canonical_sha256":"2a6f327846b690a3165c2e7ae6539a4128e1afc5e3176c79e46aaaf9794f4813","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2a6f327846b690a3165c2e7ae6539a4128e1afc5e3176c79e46aaaf9794f4813","first_computed_at":"2026-07-05T10:37:48.781027Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:37:48.781027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"v0uM3rxjcGx0tgUwBrcuCMGmRiZX1cZdEHPoYs/Q2I6SGZxSaIXLsUxsNrBx/8am5WcEsxIAQGiGQmsP1Fe7BA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:37:48.781840Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.16485","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e8552c5c1b3f0efed10472d7dc0fa5a3f8b394decc09963df7b79b79b15194dc","sha256:f28ba8be7c9bd19afa322bbe82a08cb39673dec07ff4481c38dd7a64737f9b97"],"state_sha256":"801e16bdf74b095b0064dae057ec6e84c16f6817442c8f71b4905ef0eda6429f"}