{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:UG6OCT22JCKOQHLYFFLAEUTB24","short_pith_number":"pith:UG6OCT22","schema_version":"1.0","canonical_sha256":"a1bce14f5a4894e81d782956025261d70ce9fa9a049f1cba1ac2d9d57a260080","source":{"kind":"arxiv","id":"1411.0149","version":3},"attestation_state":"computed","paper":{"title":"How Many Workers to Ask? Adaptive Exploration for Collecting High Quality Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"cs.AI","authors_text":"Aleksandrs Slivkins, Ittai Abraham, Omar Alonso, Rajesh Patel, Steven Shelford, Vasilis Kandylas","submitted_at":"2014-11-01T18:28:49Z","abstract_excerpt":"Crowdsourcing has been part of the IR toolbox as a cheap and fast mechanism to obtain labels for system development and evaluation. Successful deployment of crowdsourcing at scale involves adjusting many variables, a very important one being the number of workers needed per human intelligence task (HIT). We consider the crowdsourcing task of learning the answer to simple multiple-choice HITs, which are representative of many relevance experiments. In order to provide statistically significant results, one often needs to ask multiple workers to answer the same HIT. A stopping rule is an algorit"},"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":"1411.0149","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2014-11-01T18:28:49Z","cross_cats_sorted":["cs.DS"],"title_canon_sha256":"b757abf2ea6ca7856af862555b304961d817674c59f353b7019c4bbc968aa0f1","abstract_canon_sha256":"1df56f0604f8baa34f77dd7819f301f4691d726c0c6656f86bbcf2f409df48bd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:14:29.619941Z","signature_b64":"mRXasE7W4bMP7o8/fUw2NwhAjLuWyWvs79WHQAYTcUGdpt3snZ7EhgWGVn8/woSF09Exl3Zj4ahBbOrPL77tDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a1bce14f5a4894e81d782956025261d70ce9fa9a049f1cba1ac2d9d57a260080","last_reissued_at":"2026-05-18T01:14:29.619340Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:14:29.619340Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"How Many Workers to Ask? Adaptive Exploration for Collecting High Quality Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"cs.AI","authors_text":"Aleksandrs Slivkins, Ittai Abraham, Omar Alonso, Rajesh Patel, Steven Shelford, Vasilis Kandylas","submitted_at":"2014-11-01T18:28:49Z","abstract_excerpt":"Crowdsourcing has been part of the IR toolbox as a cheap and fast mechanism to obtain labels for system development and evaluation. Successful deployment of crowdsourcing at scale involves adjusting many variables, a very important one being the number of workers needed per human intelligence task (HIT). We consider the crowdsourcing task of learning the answer to simple multiple-choice HITs, which are representative of many relevance experiments. In order to provide statistically significant results, one often needs to ask multiple workers to answer the same HIT. A stopping rule is an algorit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1411.0149","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1411.0149","created_at":"2026-05-18T01:14:29.619423+00:00"},{"alias_kind":"arxiv_version","alias_value":"1411.0149v3","created_at":"2026-05-18T01:14:29.619423+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1411.0149","created_at":"2026-05-18T01:14:29.619423+00:00"},{"alias_kind":"pith_short_12","alias_value":"UG6OCT22JCKO","created_at":"2026-05-18T12:28:52.271510+00:00"},{"alias_kind":"pith_short_16","alias_value":"UG6OCT22JCKOQHLY","created_at":"2026-05-18T12:28:52.271510+00:00"},{"alias_kind":"pith_short_8","alias_value":"UG6OCT22","created_at":"2026-05-18T12:28:52.271510+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UG6OCT22JCKOQHLYFFLAEUTB24","json":"https://pith.science/pith/UG6OCT22JCKOQHLYFFLAEUTB24.json","graph_json":"https://pith.science/api/pith-number/UG6OCT22JCKOQHLYFFLAEUTB24/graph.json","events_json":"https://pith.science/api/pith-number/UG6OCT22JCKOQHLYFFLAEUTB24/events.json","paper":"https://pith.science/paper/UG6OCT22"},"agent_actions":{"view_html":"https://pith.science/pith/UG6OCT22JCKOQHLYFFLAEUTB24","download_json":"https://pith.science/pith/UG6OCT22JCKOQHLYFFLAEUTB24.json","view_paper":"https://pith.science/paper/UG6OCT22","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1411.0149&json=true","fetch_graph":"https://pith.science/api/pith-number/UG6OCT22JCKOQHLYFFLAEUTB24/graph.json","fetch_events":"https://pith.science/api/pith-number/UG6OCT22JCKOQHLYFFLAEUTB24/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UG6OCT22JCKOQHLYFFLAEUTB24/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UG6OCT22JCKOQHLYFFLAEUTB24/action/storage_attestation","attest_author":"https://pith.science/pith/UG6OCT22JCKOQHLYFFLAEUTB24/action/author_attestation","sign_citation":"https://pith.science/pith/UG6OCT22JCKOQHLYFFLAEUTB24/action/citation_signature","submit_replication":"https://pith.science/pith/UG6OCT22JCKOQHLYFFLAEUTB24/action/replication_record"}},"created_at":"2026-05-18T01:14:29.619423+00:00","updated_at":"2026-05-18T01:14:29.619423+00:00"}