{
  "slug": "using-machine-learning-and-disambiguated-author-identifiers-to-improve-record-linkage-for-funding-program-evaluation",
  "title": "Using Machine Learning and Disambiguated Author Identifiers to Improve Record Linkage for Funding Program Evaluation",
  "authors": "with Brandon Sepulvado and Jennifer Hamilton",
  "venue": "ASIS&T SIG-MET Annuals of Metrics",
  "year": 2021,
  "category": "survey",
  "section": "other",
  "status": "published",
  "display_status": "Conference paper",
  "visibility": "metadata-only",
  "doi": null,
  "canonical_url": "https://www.asist.org/sig/sigmet/events/",
  "summary": "This conference paper examines how machine learning and disambiguated author identifiers can improve record linkage in large-scale funding program evaluation. The goal is to reduce linkage error in publication and administrative data so that evaluation pipelines can recover research outputs and downstream impact more accurately.",
  "metadata_url": "https://jyl19.github.io/papers/using-machine-learning-and-disambiguated-author-identifiers-to-improve-record-linkage-for-funding-program-evaluation/metadata.json",
  "summary_url": "https://jyl19.github.io/papers/using-machine-learning-and-disambiguated-author-identifiers-to-improve-record-linkage-for-funding-program-evaluation/summary.md",
  "pdf_url": null
}