{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ORNU4AMECS35UTHNXHAONGAA2N","short_pith_number":"pith:ORNU4AME","schema_version":"1.0","canonical_sha256":"745b4e018414b7da4cedb9c0e69800d3466139b307f3add7e9ecb2c58944e8df","source":{"kind":"arxiv","id":"2306.17575","version":1},"attestation_state":"computed","paper":{"title":"Augmenting Holistic Review in University Admission using Natural Language Processing for Essays and Recommendation Letters","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Bradon Thymes, Jinsook Lee, Joyce Zhou, Rene F. Kizilcec, Thorsten Joachims","submitted_at":"2023-06-30T11:51:08Z","abstract_excerpt":"University admission at many highly selective institutions uses a holistic review process, where all aspects of the application, including protected attributes (e.g., race, gender), grades, essays, and recommendation letters are considered, to compose an excellent and diverse class. In this study, we empirically evaluate how influential protected attributes are for predicting admission decisions using a machine learning (ML) model, and in how far textual information (e.g., personal essay, teacher recommendation) may substitute for the loss of protected attributes in the model. Using data from "},"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":"2306.17575","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2023-06-30T11:51:08Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"a9c86787d4421005940505dada3d983dff8a0ea33b19da641510b6213d6e5874","abstract_canon_sha256":"04528b8e4e23eaf29595b7ffc656411f525da04bbc75cd316491aa4254055f13"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:26:29.348990Z","signature_b64":"Ty0yz24adsF3moorfVisi6btmGmqkDGk4hC0FjXSl9pGkToLuQJ+U8mvIHZFe2YrMJR5d5XkJOZGu3nZuV4lDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"745b4e018414b7da4cedb9c0e69800d3466139b307f3add7e9ecb2c58944e8df","last_reissued_at":"2026-07-05T06:26:29.348518Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:26:29.348518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Augmenting Holistic Review in University Admission using Natural Language Processing for Essays and Recommendation Letters","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Bradon Thymes, Jinsook Lee, Joyce Zhou, Rene F. Kizilcec, Thorsten Joachims","submitted_at":"2023-06-30T11:51:08Z","abstract_excerpt":"University admission at many highly selective institutions uses a holistic review process, where all aspects of the application, including protected attributes (e.g., race, gender), grades, essays, and recommendation letters are considered, to compose an excellent and diverse class. In this study, we empirically evaluate how influential protected attributes are for predicting admission decisions using a machine learning (ML) model, and in how far textual information (e.g., personal essay, teacher recommendation) may substitute for the loss of protected attributes in the model. Using data from "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.17575","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/2306.17575/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":"2306.17575","created_at":"2026-07-05T06:26:29.348579+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.17575v1","created_at":"2026-07-05T06:26:29.348579+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.17575","created_at":"2026-07-05T06:26:29.348579+00:00"},{"alias_kind":"pith_short_12","alias_value":"ORNU4AMECS35","created_at":"2026-07-05T06:26:29.348579+00:00"},{"alias_kind":"pith_short_16","alias_value":"ORNU4AMECS35UTHN","created_at":"2026-07-05T06:26:29.348579+00:00"},{"alias_kind":"pith_short_8","alias_value":"ORNU4AME","created_at":"2026-07-05T06:26:29.348579+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2510.07478","citing_title":"Fixed Points and Stochastic Meritocracies: A Long-Term Perspective","ref_index":8969,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ORNU4AMECS35UTHNXHAONGAA2N","json":"https://pith.science/pith/ORNU4AMECS35UTHNXHAONGAA2N.json","graph_json":"https://pith.science/api/pith-number/ORNU4AMECS35UTHNXHAONGAA2N/graph.json","events_json":"https://pith.science/api/pith-number/ORNU4AMECS35UTHNXHAONGAA2N/events.json","paper":"https://pith.science/paper/ORNU4AME"},"agent_actions":{"view_html":"https://pith.science/pith/ORNU4AMECS35UTHNXHAONGAA2N","download_json":"https://pith.science/pith/ORNU4AMECS35UTHNXHAONGAA2N.json","view_paper":"https://pith.science/paper/ORNU4AME","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.17575&json=true","fetch_graph":"https://pith.science/api/pith-number/ORNU4AMECS35UTHNXHAONGAA2N/graph.json","fetch_events":"https://pith.science/api/pith-number/ORNU4AMECS35UTHNXHAONGAA2N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ORNU4AMECS35UTHNXHAONGAA2N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ORNU4AMECS35UTHNXHAONGAA2N/action/storage_attestation","attest_author":"https://pith.science/pith/ORNU4AMECS35UTHNXHAONGAA2N/action/author_attestation","sign_citation":"https://pith.science/pith/ORNU4AMECS35UTHNXHAONGAA2N/action/citation_signature","submit_replication":"https://pith.science/pith/ORNU4AMECS35UTHNXHAONGAA2N/action/replication_record"}},"created_at":"2026-07-05T06:26:29.348579+00:00","updated_at":"2026-07-05T06:26:29.348579+00:00"}