{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HP3OGVSNMBDFFLL3JGMI2XLPOC","short_pith_number":"pith:HP3OGVSN","schema_version":"1.0","canonical_sha256":"3bf6e3564d604652ad7b49988d5d6f70a3e798720df28a0ecb1a17105ecd7cfd","source":{"kind":"arxiv","id":"2508.14764","version":2},"attestation_state":"computed","paper":{"title":"Investigation of the Inter-Rater Reliability between Large Language Models and Human Raters in Qualitative Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.ed-ph","authors_text":"Nikhil Sanjay Borse, N. Sanjay Rebello, Ravishankar Chatta Subramaniam","submitted_at":"2025-08-20T15:12:52Z","abstract_excerpt":"Qualitative analysis is typically limited to small datasets because it is time-intensive. Moreover, a second human rater is required to ensure reliable findings. Artificial intelligence tools may replace human raters if we demonstrate high reliability compared to human ratings. We investigated the inter-rater reliability of state-of-the-art Large Language Models (LLMs), ChatGPT-4o and ChatGPT-4.5-preview, in rating audio transcripts coded manually. We explored prompts and hyperparameters to optimize model performance. The participants were 14 undergraduate student groups from a university in t"},"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":"2508.14764","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ed-ph","submitted_at":"2025-08-20T15:12:52Z","cross_cats_sorted":[],"title_canon_sha256":"df85d6c485b6c478e4f6808908090f55df17418668eab281b2637b2b417dcedd","abstract_canon_sha256":"2595f907f8803a86c2a897775d59259b6c1dd5548e20d288c3ce5d327b060d09"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:02:06.448441Z","signature_b64":"WgDjnsB5PUt14TxUd2AZ05BmP7h1JihPrqlAynjBiuZoDtPzFi6jv5DsLGnAy66A9R108f6ib21U6jAUY6jYAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3bf6e3564d604652ad7b49988d5d6f70a3e798720df28a0ecb1a17105ecd7cfd","last_reissued_at":"2026-07-05T12:02:06.447884Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:02:06.447884Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Investigation of the Inter-Rater Reliability between Large Language Models and Human Raters in Qualitative Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.ed-ph","authors_text":"Nikhil Sanjay Borse, N. Sanjay Rebello, Ravishankar Chatta Subramaniam","submitted_at":"2025-08-20T15:12:52Z","abstract_excerpt":"Qualitative analysis is typically limited to small datasets because it is time-intensive. Moreover, a second human rater is required to ensure reliable findings. Artificial intelligence tools may replace human raters if we demonstrate high reliability compared to human ratings. We investigated the inter-rater reliability of state-of-the-art Large Language Models (LLMs), ChatGPT-4o and ChatGPT-4.5-preview, in rating audio transcripts coded manually. We explored prompts and hyperparameters to optimize model performance. The participants were 14 undergraduate student groups from a university in t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.14764","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/2508.14764/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":"2508.14764","created_at":"2026-07-05T12:02:06.447947+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.14764v2","created_at":"2026-07-05T12:02:06.447947+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.14764","created_at":"2026-07-05T12:02:06.447947+00:00"},{"alias_kind":"pith_short_12","alias_value":"HP3OGVSNMBDF","created_at":"2026-07-05T12:02:06.447947+00:00"},{"alias_kind":"pith_short_16","alias_value":"HP3OGVSNMBDFFLL3","created_at":"2026-07-05T12:02:06.447947+00:00"},{"alias_kind":"pith_short_8","alias_value":"HP3OGVSN","created_at":"2026-07-05T12:02:06.447947+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.00748","citing_title":"Me and My Bot: What Users Talk About in AI Companion Communities on Reddit","ref_index":2026,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HP3OGVSNMBDFFLL3JGMI2XLPOC","json":"https://pith.science/pith/HP3OGVSNMBDFFLL3JGMI2XLPOC.json","graph_json":"https://pith.science/api/pith-number/HP3OGVSNMBDFFLL3JGMI2XLPOC/graph.json","events_json":"https://pith.science/api/pith-number/HP3OGVSNMBDFFLL3JGMI2XLPOC/events.json","paper":"https://pith.science/paper/HP3OGVSN"},"agent_actions":{"view_html":"https://pith.science/pith/HP3OGVSNMBDFFLL3JGMI2XLPOC","download_json":"https://pith.science/pith/HP3OGVSNMBDFFLL3JGMI2XLPOC.json","view_paper":"https://pith.science/paper/HP3OGVSN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.14764&json=true","fetch_graph":"https://pith.science/api/pith-number/HP3OGVSNMBDFFLL3JGMI2XLPOC/graph.json","fetch_events":"https://pith.science/api/pith-number/HP3OGVSNMBDFFLL3JGMI2XLPOC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HP3OGVSNMBDFFLL3JGMI2XLPOC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HP3OGVSNMBDFFLL3JGMI2XLPOC/action/storage_attestation","attest_author":"https://pith.science/pith/HP3OGVSNMBDFFLL3JGMI2XLPOC/action/author_attestation","sign_citation":"https://pith.science/pith/HP3OGVSNMBDFFLL3JGMI2XLPOC/action/citation_signature","submit_replication":"https://pith.science/pith/HP3OGVSNMBDFFLL3JGMI2XLPOC/action/replication_record"}},"created_at":"2026-07-05T12:02:06.447947+00:00","updated_at":"2026-07-05T12:02:06.447947+00:00"}