{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:L5DRDHJYORUOW67OGLZRWMW4JW","short_pith_number":"pith:L5DRDHJY","schema_version":"1.0","canonical_sha256":"5f47119d387468eb7bee32f31b32dc4db264876275aed67847d94b34f030ba83","source":{"kind":"arxiv","id":"2309.00238","version":1},"attestation_state":"computed","paper":{"title":"ALJP: An Arabic Legal Judgment Prediction in Personal Status Cases Using Machine Learning Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Alhanouf Alsolami, Areej Alhothali, Aya Kazzaz, Mona Hafez, Salwa Abbara","submitted_at":"2023-09-01T04:08:45Z","abstract_excerpt":"Legal Judgment Prediction (LJP) aims to predict judgment outcomes based on case description. Several researchers have developed techniques to assist potential clients by predicting the outcome in the legal profession. However, none of the proposed techniques were implemented in Arabic, and only a few attempts were implemented in English, Chinese, and Hindi. In this paper, we develop a system that utilizes deep learning (DL) and natural language processing (NLP) techniques to predict the judgment outcome from Arabic case scripts, especially in cases of custody and annulment of marriage. This sy"},"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":"2309.00238","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2023-09-01T04:08:45Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"fee062cd79cd05864914f6a9b9234a66f7795c7a1d8856ae8f73e5798f22928f","abstract_canon_sha256":"eb21ce3cbf0b9df4234e375159794e79bbb71286a140f3c10044fa6778cb9ce9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:46:54.134019Z","signature_b64":"HnbT2R/Vbpb5dxtnyQ/asp8w/i2eT3nlycgKQom0GN4UB5HIB0kvpwYVcjOnhmRvufSdNUFWFYFM5Yw9cApeDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5f47119d387468eb7bee32f31b32dc4db264876275aed67847d94b34f030ba83","last_reissued_at":"2026-07-05T06:46:54.133477Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:46:54.133477Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ALJP: An Arabic Legal Judgment Prediction in Personal Status Cases Using Machine Learning Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Alhanouf Alsolami, Areej Alhothali, Aya Kazzaz, Mona Hafez, Salwa Abbara","submitted_at":"2023-09-01T04:08:45Z","abstract_excerpt":"Legal Judgment Prediction (LJP) aims to predict judgment outcomes based on case description. Several researchers have developed techniques to assist potential clients by predicting the outcome in the legal profession. However, none of the proposed techniques were implemented in Arabic, and only a few attempts were implemented in English, Chinese, and Hindi. In this paper, we develop a system that utilizes deep learning (DL) and natural language processing (NLP) techniques to predict the judgment outcome from Arabic case scripts, especially in cases of custody and annulment of marriage. This sy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.00238","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/2309.00238/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":"2309.00238","created_at":"2026-07-05T06:46:54.133562+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.00238v1","created_at":"2026-07-05T06:46:54.133562+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.00238","created_at":"2026-07-05T06:46:54.133562+00:00"},{"alias_kind":"pith_short_12","alias_value":"L5DRDHJYORUO","created_at":"2026-07-05T06:46:54.133562+00:00"},{"alias_kind":"pith_short_16","alias_value":"L5DRDHJYORUOW67O","created_at":"2026-07-05T06:46:54.133562+00:00"},{"alias_kind":"pith_short_8","alias_value":"L5DRDHJY","created_at":"2026-07-05T06:46:54.133562+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.21560","citing_title":"Reinforcement learning fine-tuning of language model for instruction following and math reasoning","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L5DRDHJYORUOW67OGLZRWMW4JW","json":"https://pith.science/pith/L5DRDHJYORUOW67OGLZRWMW4JW.json","graph_json":"https://pith.science/api/pith-number/L5DRDHJYORUOW67OGLZRWMW4JW/graph.json","events_json":"https://pith.science/api/pith-number/L5DRDHJYORUOW67OGLZRWMW4JW/events.json","paper":"https://pith.science/paper/L5DRDHJY"},"agent_actions":{"view_html":"https://pith.science/pith/L5DRDHJYORUOW67OGLZRWMW4JW","download_json":"https://pith.science/pith/L5DRDHJYORUOW67OGLZRWMW4JW.json","view_paper":"https://pith.science/paper/L5DRDHJY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.00238&json=true","fetch_graph":"https://pith.science/api/pith-number/L5DRDHJYORUOW67OGLZRWMW4JW/graph.json","fetch_events":"https://pith.science/api/pith-number/L5DRDHJYORUOW67OGLZRWMW4JW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L5DRDHJYORUOW67OGLZRWMW4JW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L5DRDHJYORUOW67OGLZRWMW4JW/action/storage_attestation","attest_author":"https://pith.science/pith/L5DRDHJYORUOW67OGLZRWMW4JW/action/author_attestation","sign_citation":"https://pith.science/pith/L5DRDHJYORUOW67OGLZRWMW4JW/action/citation_signature","submit_replication":"https://pith.science/pith/L5DRDHJYORUOW67OGLZRWMW4JW/action/replication_record"}},"created_at":"2026-07-05T06:46:54.133562+00:00","updated_at":"2026-07-05T06:46:54.133562+00:00"}