{"id":12013837,"identifier":"DVN/GM8T8Q","persistentUrl":"https://doi.org/10.7910/DVN/GM8T8Q","protocol":"doi","authority":"10.7910","separator":"/","publisher":"Harvard Dataverse","publicationDate":"2025-08-26","storageIdentifier":"s3://10.7910/DVN/GM8T8Q","datasetType":"dataset","datasetVersion":{"id":501743,"datasetId":12013837,"datasetPersistentId":"doi:10.7910/DVN/GM8T8Q","datasetType":"dataset","storageIdentifier":"s3://10.7910/DVN/GM8T8Q","versionNumber":1,"internalVersionNumber":4,"versionMinorNumber":1,"versionState":"RELEASED","latestVersionPublishingState":"RELEASED","lastUpdateTime":"2025-08-28T01:38:22Z","releaseTime":"2025-08-28T01:38:22Z","createTime":"2025-08-27T09:03:56Z","publicationDate":"2025-08-26","citationDate":"2025-08-26","license":{"name":"CC0 1.0","uri":"http://creativecommons.org/publicdomain/zero/1.0","iconUri":"https://licensebuttons.net/p/zero/1.0/88x31.png","rightsIdentifier":"CC0-1.0","rightsIdentifierScheme":"SPDX","schemeUri":"https://spdx.org/licenses/","languageCode":"en"},"fileAccessRequest":true,"metadataBlocks":{"citation":{"displayName":"Citation Metadata","name":"citation","fields":[{"typeName":"title","multiple":false,"typeClass":"primitive","value":"Replication Data for: Automated Quality Assessment for LLM-Based Complex Qualitative Coding: A Confidence-Diversity Framework"},{"typeName":"author","multiple":true,"typeClass":"compound","value":[{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"zhao, zhilong"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"https://ror.org/0530pts50","expandedvalue":{"scheme":"http://www.grid.ac/ontology/","termName":"South China University of Technology","@type":"https://schema.org/Organization"}}}]},{"typeName":"datasetContact","multiple":true,"typeClass":"compound","value":[{"datasetContactName":{"typeName":"datasetContactName","multiple":false,"typeClass":"primitive","value":"zhao, zhilong"},"datasetContactEmail":{"typeName":"datasetContactEmail","multiple":false,"typeClass":"primitive","value":"yb87315@umac.mo"}}]},{"typeName":"dsDescription","multiple":true,"typeClass":"compound","value":[{"dsDescriptionValue":{"typeName":"dsDescriptionValue","multiple":false,"typeClass":"primitive","value":"This replication package contains all code and data necessary to reproduce the results presented in \"Cross-Domain Quality Assessment for Complex Qualitative Analysis: Validating Confidence-Entropy Signals Across Legal, Political, and Medical Tasks\".\n\nResearch Context: This study extends beyond accessible coding tasks to validate automated quality assessment for complex qualitative analysis requiring domain expertise and interpretive judgment across legal, political, and medical domains.\n\nPackage Contents:\n- Core Scripts: reproduce_all_results.py (main reproduction script), generate_synthetic_data.py (data generator), validate_reproduction.py (result validation)\n- Data Files: Synthetic datasets matching paper statistics for SCOTUS legal reasoning (390 cases), Hyperpartisan political analysis (644 cases), and MTSamples medical classification (1,000 cases)\n- Expected Outputs: All LaTeX tables (Table 1-5), validation reports, and cross-domain statistical analyses\n\nKey Findings Reproduced:\n- Cross-domain signal effectiveness (Table 1): Perfect correlation reproduction across all domains (±0.005 accuracy)\n- Dual-signal weight optimization (Table 2): 6.6-113.7% improvements over single-signal baselines\n- Cross-domain transferability (Table 3): 88.9% success rate for weight transfer across domains\n- Intelligent triage efficiency (Table 5): 45.4% vs 44.6% effort reduction (0.8% difference)\n- Domain-specific patterns: Confidence signals are stronger in legal contexts, and entropy signals are more reliable in political/medical domains\n\nValidation Status: Successfully reproduces all core findings with statistical significance maintained across complex analytical tasks. Demonstrates automated quality assessment viability for scaling complex qualitative research beyond accessible coding tasks.\n\nUsage: Run ./run_complete_reproduction.sh for complete reproduction, or python3 reproduce_all_results.py for individual table generation. All dependencies included."}}]},{"typeName":"subject","multiple":true,"typeClass":"controlledVocabulary","value":["Computer and Information Science","Social Sciences"]},{"typeName":"depositor","multiple":false,"typeClass":"primitive","value":"zhao, zhilong"},{"typeName":"dateOfDeposit","multiple":false,"typeClass":"primitive","value":"2025-08-26"}]}},"files":[{"label":"reproduction_package.zip","restricted":false,"version":1,"datasetVersionId":501743,"dataFile":{"id":12013838,"persistentId":"","filename":"reproduction_package.zip","contentType":"application/zip","friendlyType":"ZIP Archive","filesize":124689,"storageIdentifier":"s3://dvn-cloud:198e5504712-ccab87a56914","rootDataFileId":-1,"md5":"cf2c64c662be72d7ea11152c7c4845ad","checksum":{"type":"MD5","value":"cf2c64c662be72d7ea11152c7c4845ad"},"tabularData":false,"creationDate":"2025-08-26","publicationDate":"2025-08-26","fileAccessRequest":true}}],"citation":"zhao, zhilong, 2025, \"Replication Data for: Automated Quality Assessment for LLM-Based Complex Qualitative Coding: A Confidence-Diversity Framework\", https://doi.org/10.7910/DVN/GM8T8Q, Harvard Dataverse, V1"}}