{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:E232BAAVMP4R7L33CAN47FVJKF","short_pith_number":"pith:E232BAAV","canonical_record":{"source":{"id":"2502.09294","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-13T13:08:42Z","cross_cats_sorted":[],"title_canon_sha256":"cb0ab5332d3f0de4dc83bdbf88308e70d5bc51ba97fdbc81d63757271ce7958a","abstract_canon_sha256":"ff9217adce0e08b1f8a49dff9152156adea5cc7594a55e8bc5003ad58ad24b26"},"schema_version":"1.0"},"canonical_sha256":"26b7a0801563f91faf7b101bcf96a9515b832ad0feacd3978a0fc0d3fa12dc79","source":{"kind":"arxiv","id":"2502.09294","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.09294","created_at":"2026-07-05T10:13:55Z"},{"alias_kind":"arxiv_version","alias_value":"2502.09294v1","created_at":"2026-07-05T10:13:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.09294","created_at":"2026-07-05T10:13:55Z"},{"alias_kind":"pith_short_12","alias_value":"E232BAAVMP4R","created_at":"2026-07-05T10:13:55Z"},{"alias_kind":"pith_short_16","alias_value":"E232BAAVMP4R7L33","created_at":"2026-07-05T10:13:55Z"},{"alias_kind":"pith_short_8","alias_value":"E232BAAV","created_at":"2026-07-05T10:13:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:E232BAAVMP4R7L33CAN47FVJKF","target":"record","payload":{"canonical_record":{"source":{"id":"2502.09294","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-13T13:08:42Z","cross_cats_sorted":[],"title_canon_sha256":"cb0ab5332d3f0de4dc83bdbf88308e70d5bc51ba97fdbc81d63757271ce7958a","abstract_canon_sha256":"ff9217adce0e08b1f8a49dff9152156adea5cc7594a55e8bc5003ad58ad24b26"},"schema_version":"1.0"},"canonical_sha256":"26b7a0801563f91faf7b101bcf96a9515b832ad0feacd3978a0fc0d3fa12dc79","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:13:55.017641Z","signature_b64":"vodlkzjT5vXRH/47f7wRwHTajuDRUrZx5/mW0YHSRnYXn4pypQDBXX16masZW4c29JCiyPliT/XObYIkCKXDCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"26b7a0801563f91faf7b101bcf96a9515b832ad0feacd3978a0fc0d3fa12dc79","last_reissued_at":"2026-07-05T10:13:55.017056Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:13:55.017056Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.09294","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":"2026-07-05T10:13:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GATTzHxponVLCU9STEY18WSoj191RWuYKcrjpr1Ws4dBIj7Npcrr0gYMMdNVn1kLi7PUMd2vADPXkxt17/qyCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T12:25:55.998860Z"},"content_sha256":"813cdf166774e101321375b5e345bf5fc285d713592328fe3213ade7a677e5f7","schema_version":"1.0","event_id":"sha256:813cdf166774e101321375b5e345bf5fc285d713592328fe3213ade7a677e5f7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:E232BAAVMP4R7L33CAN47FVJKF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Indeterminacy in Affective Computing: Considering Meaning and Context in Data Collection Practices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bernd Dudzik, Chenxu Hao, Chirag Raman, Masha Tsfasman, Tiffany Matej Hrkalovic","submitted_at":"2025-02-13T13:08:42Z","abstract_excerpt":"Automatic Affect Prediction (AAP) uses computational analysis of input data such as text, speech, images, and physiological signals to predict various affective phenomena (e.g., emotions or moods). These models are typically constructed using supervised machine-learning algorithms, which rely heavily on labeled training datasets. In this position paper, we posit that all AAP training data are derived from human Affective Interpretation Processes, resulting in a form of Affective Meaning. Research on human affect indicates a form of complexity that is fundamental to such meaning: it can possess"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.09294","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/2502.09294/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":"2026-07-05T10:13:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+1z+8L/kErQ2il4qJR49zZPPdPhP1oJ9XWATeFf/P8qVm9MAeBP7t9DqEnbv2o+qDxO6O/uQ1mL5rUxc9tDcCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T12:25:55.999347Z"},"content_sha256":"1ab99c9414f53831b32b6ef323c494f53f8bfa18f1f255389ac6acede8bf2892","schema_version":"1.0","event_id":"sha256:1ab99c9414f53831b32b6ef323c494f53f8bfa18f1f255389ac6acede8bf2892"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E232BAAVMP4R7L33CAN47FVJKF/bundle.json","state_url":"https://pith.science/pith/E232BAAVMP4R7L33CAN47FVJKF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E232BAAVMP4R7L33CAN47FVJKF/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":"2026-08-08T12:25:56Z","links":{"resolver":"https://pith.science/pith/E232BAAVMP4R7L33CAN47FVJKF","bundle":"https://pith.science/pith/E232BAAVMP4R7L33CAN47FVJKF/bundle.json","state":"https://pith.science/pith/E232BAAVMP4R7L33CAN47FVJKF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E232BAAVMP4R7L33CAN47FVJKF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:E232BAAVMP4R7L33CAN47FVJKF","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":"ff9217adce0e08b1f8a49dff9152156adea5cc7594a55e8bc5003ad58ad24b26","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-13T13:08:42Z","title_canon_sha256":"cb0ab5332d3f0de4dc83bdbf88308e70d5bc51ba97fdbc81d63757271ce7958a"},"schema_version":"1.0","source":{"id":"2502.09294","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.09294","created_at":"2026-07-05T10:13:55Z"},{"alias_kind":"arxiv_version","alias_value":"2502.09294v1","created_at":"2026-07-05T10:13:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.09294","created_at":"2026-07-05T10:13:55Z"},{"alias_kind":"pith_short_12","alias_value":"E232BAAVMP4R","created_at":"2026-07-05T10:13:55Z"},{"alias_kind":"pith_short_16","alias_value":"E232BAAVMP4R7L33","created_at":"2026-07-05T10:13:55Z"},{"alias_kind":"pith_short_8","alias_value":"E232BAAV","created_at":"2026-07-05T10:13:55Z"}],"graph_snapshots":[{"event_id":"sha256:1ab99c9414f53831b32b6ef323c494f53f8bfa18f1f255389ac6acede8bf2892","target":"graph","created_at":"2026-07-05T10:13:55Z","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/2502.09294/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Automatic Affect Prediction (AAP) uses computational analysis of input data such as text, speech, images, and physiological signals to predict various affective phenomena (e.g., emotions or moods). These models are typically constructed using supervised machine-learning algorithms, which rely heavily on labeled training datasets. In this position paper, we posit that all AAP training data are derived from human Affective Interpretation Processes, resulting in a form of Affective Meaning. Research on human affect indicates a form of complexity that is fundamental to such meaning: it can possess","authors_text":"Bernd Dudzik, Chenxu Hao, Chirag Raman, Masha Tsfasman, Tiffany Matej Hrkalovic","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-13T13:08:42Z","title":"Indeterminacy in Affective Computing: Considering Meaning and Context in Data Collection Practices"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.09294","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":"sha256:813cdf166774e101321375b5e345bf5fc285d713592328fe3213ade7a677e5f7","target":"record","created_at":"2026-07-05T10:13:55Z","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":"ff9217adce0e08b1f8a49dff9152156adea5cc7594a55e8bc5003ad58ad24b26","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-13T13:08:42Z","title_canon_sha256":"cb0ab5332d3f0de4dc83bdbf88308e70d5bc51ba97fdbc81d63757271ce7958a"},"schema_version":"1.0","source":{"id":"2502.09294","kind":"arxiv","version":1}},"canonical_sha256":"26b7a0801563f91faf7b101bcf96a9515b832ad0feacd3978a0fc0d3fa12dc79","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"26b7a0801563f91faf7b101bcf96a9515b832ad0feacd3978a0fc0d3fa12dc79","first_computed_at":"2026-07-05T10:13:55.017056Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:13:55.017056Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vodlkzjT5vXRH/47f7wRwHTajuDRUrZx5/mW0YHSRnYXn4pypQDBXX16masZW4c29JCiyPliT/XObYIkCKXDCg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:13:55.017641Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.09294","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:813cdf166774e101321375b5e345bf5fc285d713592328fe3213ade7a677e5f7","sha256:1ab99c9414f53831b32b6ef323c494f53f8bfa18f1f255389ac6acede8bf2892"],"state_sha256":"e6f2ac960bd54073ad602502eb06ab144ca3c49a8535caca299848c394acd06c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EwpqNuWDeXBKScW3Azs9Fs6laelFG7EUOCtLyxrcT6BrpogD50WdJtezy68wDo8W/TWP60ITmuDd3/uRVa7nBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T12:25:56.003109Z","bundle_sha256":"c1f4b933567abc2017479be7150f22355b052b2aecdd4cfc6e6c3b24f5835adb"}}