# Using Machine Learning and Disambiguated Author Identifiers to Improve Record Linkage for Funding Program Evaluation

Status: conference paper
Visibility: metadata-only
Authors: with Brandon Sepulvado and Jennifer Hamilton
Venue: ASIS&T SIG-MET Annuals of Metrics
Year: 2021
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.

## Public Files

- Metadata: https://jyl19.github.io/papers/using-machine-learning-and-disambiguated-author-identifiers-to-improve-record-linkage-for-funding-program-evaluation/metadata.json
- Canonical URL: https://www.asist.org/sig/sigmet/events/
- PDF: not yet staged for public release