{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:F2V7XU4FCQLDV2ILB5CJIEDKHZ","short_pith_number":"pith:F2V7XU4F","canonical_record":{"source":{"id":"2506.06540","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2025-06-06T21:14:57Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"2c43559842ebc0027d6c8045a36dc82d661fd5b7947477b79280802013f5abba","abstract_canon_sha256":"92b404c182458deeb27f6b5e1b4073f51c135e051d74672eeaee6150dc32c04d"},"schema_version":"1.0"},"canonical_sha256":"2eabfbd38514163ae90b0f4494106a3e4b67cc818cfb8cfe3afe36d285d2729f","source":{"kind":"arxiv","id":"2506.06540","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.06540","created_at":"2026-07-05T11:17:51Z"},{"alias_kind":"arxiv_version","alias_value":"2506.06540v1","created_at":"2026-07-05T11:17:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06540","created_at":"2026-07-05T11:17:51Z"},{"alias_kind":"pith_short_12","alias_value":"F2V7XU4FCQLD","created_at":"2026-07-05T11:17:51Z"},{"alias_kind":"pith_short_16","alias_value":"F2V7XU4FCQLDV2IL","created_at":"2026-07-05T11:17:51Z"},{"alias_kind":"pith_short_8","alias_value":"F2V7XU4F","created_at":"2026-07-05T11:17:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:F2V7XU4FCQLDV2ILB5CJIEDKHZ","target":"record","payload":{"canonical_record":{"source":{"id":"2506.06540","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2025-06-06T21:14:57Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"2c43559842ebc0027d6c8045a36dc82d661fd5b7947477b79280802013f5abba","abstract_canon_sha256":"92b404c182458deeb27f6b5e1b4073f51c135e051d74672eeaee6150dc32c04d"},"schema_version":"1.0"},"canonical_sha256":"2eabfbd38514163ae90b0f4494106a3e4b67cc818cfb8cfe3afe36d285d2729f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:51.879130Z","signature_b64":"WUttrQHwXsZV2vHIo/gHCM62tKvxUl6xU5q+CIAyEL66s61dzLqgTQiw2N4x7augPxXnMZ2sjQ799x4FB1v3Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2eabfbd38514163ae90b0f4494106a3e4b67cc818cfb8cfe3afe36d285d2729f","last_reissued_at":"2026-07-05T11:17:51.878743Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:51.878743Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.06540","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:17:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xpB3UnGxghRkWYOF9XSViYeVW+lwPcGiH1SVaymh0AB943+Ow9hO7pQA5Rglqayb8IzFoSBHmGAtzbxlArylCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T03:20:30.813229Z"},"content_sha256":"1923ef5cefb8a003905d4f1da0a22ef12caa05ea22c229ac020d6f77c78de535","schema_version":"1.0","event_id":"sha256:1923ef5cefb8a003905d4f1da0a22ef12caa05ea22c229ac020d6f77c78de535"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:F2V7XU4FCQLDV2ILB5CJIEDKHZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Large Language Models Can Be a Viable Substitute for Expert Political Surveys When a Shock Disrupts Traditional Measurement Approaches","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.CY","authors_text":"Patrick Y. Wu","submitted_at":"2025-06-06T21:14:57Z","abstract_excerpt":"After a disruptive event or shock, such as the Department of Government Efficiency (DOGE) federal layoffs of 2025, expert judgments are colored by knowledge of the outcome. This can make it difficult or impossible to reconstruct the pre-event perceptions needed to study the factors associated with the event. This position paper argues that large language models (LLMs), trained on vast amounts of digital media data, can be a viable substitute for expert political surveys when a shock disrupts traditional measurement. We analyze the DOGE layoffs as a specific case study for this position. We use"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06540","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/2506.06540/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:17:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8nSdEi1BLxjKulXNRMpg7qIH5OX+JOPCMyRy8uqwOmUrLndNi0z/WtIuJ+hqL8dwtHiZ0mio56CG4FiMSzmxBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T03:20:30.813753Z"},"content_sha256":"ba280774f25c319fa6861b010cb45232947be30dc6a81dd7a9782a811fc0d2a4","schema_version":"1.0","event_id":"sha256:ba280774f25c319fa6861b010cb45232947be30dc6a81dd7a9782a811fc0d2a4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F2V7XU4FCQLDV2ILB5CJIEDKHZ/bundle.json","state_url":"https://pith.science/pith/F2V7XU4FCQLDV2ILB5CJIEDKHZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F2V7XU4FCQLDV2ILB5CJIEDKHZ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T03:20:30Z","links":{"resolver":"https://pith.science/pith/F2V7XU4FCQLDV2ILB5CJIEDKHZ","bundle":"https://pith.science/pith/F2V7XU4FCQLDV2ILB5CJIEDKHZ/bundle.json","state":"https://pith.science/pith/F2V7XU4FCQLDV2ILB5CJIEDKHZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F2V7XU4FCQLDV2ILB5CJIEDKHZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:F2V7XU4FCQLDV2ILB5CJIEDKHZ","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":"92b404c182458deeb27f6b5e1b4073f51c135e051d74672eeaee6150dc32c04d","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2025-06-06T21:14:57Z","title_canon_sha256":"2c43559842ebc0027d6c8045a36dc82d661fd5b7947477b79280802013f5abba"},"schema_version":"1.0","source":{"id":"2506.06540","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.06540","created_at":"2026-07-05T11:17:51Z"},{"alias_kind":"arxiv_version","alias_value":"2506.06540v1","created_at":"2026-07-05T11:17:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06540","created_at":"2026-07-05T11:17:51Z"},{"alias_kind":"pith_short_12","alias_value":"F2V7XU4FCQLD","created_at":"2026-07-05T11:17:51Z"},{"alias_kind":"pith_short_16","alias_value":"F2V7XU4FCQLDV2IL","created_at":"2026-07-05T11:17:51Z"},{"alias_kind":"pith_short_8","alias_value":"F2V7XU4F","created_at":"2026-07-05T11:17:51Z"}],"graph_snapshots":[{"event_id":"sha256:ba280774f25c319fa6861b010cb45232947be30dc6a81dd7a9782a811fc0d2a4","target":"graph","created_at":"2026-07-05T11:17:51Z","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/2506.06540/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"After a disruptive event or shock, such as the Department of Government Efficiency (DOGE) federal layoffs of 2025, expert judgments are colored by knowledge of the outcome. This can make it difficult or impossible to reconstruct the pre-event perceptions needed to study the factors associated with the event. This position paper argues that large language models (LLMs), trained on vast amounts of digital media data, can be a viable substitute for expert political surveys when a shock disrupts traditional measurement. We analyze the DOGE layoffs as a specific case study for this position. We use","authors_text":"Patrick Y. Wu","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2025-06-06T21:14:57Z","title":"Large Language Models Can Be a Viable Substitute for Expert Political Surveys When a Shock Disrupts Traditional Measurement Approaches"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06540","kind":"arxiv","version":1},"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":"sha256:1923ef5cefb8a003905d4f1da0a22ef12caa05ea22c229ac020d6f77c78de535","target":"record","created_at":"2026-07-05T11:17:51Z","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":"92b404c182458deeb27f6b5e1b4073f51c135e051d74672eeaee6150dc32c04d","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2025-06-06T21:14:57Z","title_canon_sha256":"2c43559842ebc0027d6c8045a36dc82d661fd5b7947477b79280802013f5abba"},"schema_version":"1.0","source":{"id":"2506.06540","kind":"arxiv","version":1}},"canonical_sha256":"2eabfbd38514163ae90b0f4494106a3e4b67cc818cfb8cfe3afe36d285d2729f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2eabfbd38514163ae90b0f4494106a3e4b67cc818cfb8cfe3afe36d285d2729f","first_computed_at":"2026-07-05T11:17:51.878743Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:17:51.878743Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WUttrQHwXsZV2vHIo/gHCM62tKvxUl6xU5q+CIAyEL66s61dzLqgTQiw2N4x7augPxXnMZ2sjQ799x4FB1v3Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:17:51.879130Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.06540","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1923ef5cefb8a003905d4f1da0a22ef12caa05ea22c229ac020d6f77c78de535","sha256:ba280774f25c319fa6861b010cb45232947be30dc6a81dd7a9782a811fc0d2a4"],"state_sha256":"bcb55daf6b925aea5fe2e91f4a6b508fab26bb8e395dff91e350672f087bb7e3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LL27b8C8XmrYqo3qsaGZAGx9pNRpRt1+fA8COw8X1IBiwoo4YwbsawHz0ouyC6ycU7HI1qCGq9pU/i+RJp5eDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T03:20:30.817885Z","bundle_sha256":"968a83c7cb29c19a92bd9c882dbdf3486e30b680645c89516c82c5be964b70b2"}}