{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FOXYIXMLW5EO7LW6Q4X7BUTIAH","short_pith_number":"pith:FOXYIXML","schema_version":"1.0","canonical_sha256":"2baf845d8bb748efaede872ff0d26801cef38c4b110e906b03056307636d1839","source":{"kind":"arxiv","id":"2410.11399","version":2},"attestation_state":"computed","paper":{"title":"Convergence to the Truth","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ME"],"primary_cat":"stat.OT","authors_text":"Hanti Lin","submitted_at":"2024-10-15T08:44:14Z","abstract_excerpt":"This article reviews and develops an epistemological tradition in the philosophy of science, known as convergentism, which holds that inference methods should be assessed based on their ability to converge to the truth across a range of possible scenarios. Emphasis is placed on its historical origins in the work of C. S. Peirce and its recent developments in formal epistemology and data science (including statistics and machine learning). Comparisons are made with three other traditions: (1) explanationism, which holds that theory choice should be guided by a theory's overall balance of explan"},"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":"2410.11399","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.OT","submitted_at":"2024-10-15T08:44:14Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ME"],"title_canon_sha256":"3fb0f4595b98daac86a7ef9f344c05681f0bde256c8a2b2bb1005ef3a0715930","abstract_canon_sha256":"29a5345d8bc2235c2e1b6853bcdfae78fd0f4504c2636542d85befde95da86b8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:29:02.579125Z","signature_b64":"0pa2z0+BMvHJ/p/O2ouNitSZT+gsY1AWa17wNIu2WpnAUyz1+YJSNsuV2dJah+doD6Jfd9W0qY1Vo65ofNhHCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2baf845d8bb748efaede872ff0d26801cef38c4b110e906b03056307636d1839","last_reissued_at":"2026-07-05T11:29:02.578578Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:29:02.578578Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Convergence to the Truth","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ME"],"primary_cat":"stat.OT","authors_text":"Hanti Lin","submitted_at":"2024-10-15T08:44:14Z","abstract_excerpt":"This article reviews and develops an epistemological tradition in the philosophy of science, known as convergentism, which holds that inference methods should be assessed based on their ability to converge to the truth across a range of possible scenarios. Emphasis is placed on its historical origins in the work of C. S. Peirce and its recent developments in formal epistemology and data science (including statistics and machine learning). Comparisons are made with three other traditions: (1) explanationism, which holds that theory choice should be guided by a theory's overall balance of explan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.11399","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/2410.11399/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":"2410.11399","created_at":"2026-07-05T11:29:02.578640+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.11399v2","created_at":"2026-07-05T11:29:02.578640+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.11399","created_at":"2026-07-05T11:29:02.578640+00:00"},{"alias_kind":"pith_short_12","alias_value":"FOXYIXMLW5EO","created_at":"2026-07-05T11:29:02.578640+00:00"},{"alias_kind":"pith_short_16","alias_value":"FOXYIXMLW5EO7LW6","created_at":"2026-07-05T11:29:02.578640+00:00"},{"alias_kind":"pith_short_8","alias_value":"FOXYIXML","created_at":"2026-07-05T11:29:02.578640+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.15243","citing_title":"Asymptotic efficiency of inferential models and a possibilistic Bernstein--von Mises theorem","ref_index":52,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FOXYIXMLW5EO7LW6Q4X7BUTIAH","json":"https://pith.science/pith/FOXYIXMLW5EO7LW6Q4X7BUTIAH.json","graph_json":"https://pith.science/api/pith-number/FOXYIXMLW5EO7LW6Q4X7BUTIAH/graph.json","events_json":"https://pith.science/api/pith-number/FOXYIXMLW5EO7LW6Q4X7BUTIAH/events.json","paper":"https://pith.science/paper/FOXYIXML"},"agent_actions":{"view_html":"https://pith.science/pith/FOXYIXMLW5EO7LW6Q4X7BUTIAH","download_json":"https://pith.science/pith/FOXYIXMLW5EO7LW6Q4X7BUTIAH.json","view_paper":"https://pith.science/paper/FOXYIXML","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.11399&json=true","fetch_graph":"https://pith.science/api/pith-number/FOXYIXMLW5EO7LW6Q4X7BUTIAH/graph.json","fetch_events":"https://pith.science/api/pith-number/FOXYIXMLW5EO7LW6Q4X7BUTIAH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FOXYIXMLW5EO7LW6Q4X7BUTIAH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FOXYIXMLW5EO7LW6Q4X7BUTIAH/action/storage_attestation","attest_author":"https://pith.science/pith/FOXYIXMLW5EO7LW6Q4X7BUTIAH/action/author_attestation","sign_citation":"https://pith.science/pith/FOXYIXMLW5EO7LW6Q4X7BUTIAH/action/citation_signature","submit_replication":"https://pith.science/pith/FOXYIXMLW5EO7LW6Q4X7BUTIAH/action/replication_record"}},"created_at":"2026-07-05T11:29:02.578640+00:00","updated_at":"2026-07-05T11:29:02.578640+00:00"}