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      <dc:title>Equitable Metadata for Diverse Voices: Sustainable Computational Poetry Analysis with HathiTrust Extracted Features</dc:title>
      <dc:creator>Choi, Kahyun</dc:creator>
      <dc:creator>Peng, You</dc:creator>
      <dc:creator>Kang, Gyuri</dc:creator>
      <dc:description>The retirement of the HathiTrust Research Center (HTRC) infrastructure raises questions about continuing computational research on in-copyright collections in the HathiTrust Digital Library (HTDL). Since HTRC has actively supported inclusive research on underrepresented groups, the HTDL collections serve as a crucial test case for exploring post-HTRC workflows. To address this, we share an augmented dataset of American poetry by poets from historically underrepresented groups in the HTDL. Mapping this collection to HTRC Extracted Features (EF) v2.5 demonstrated that EF is highly reliable with high retrieval coverage, achieving a 100\% match. Our computational linguistic analysis shows that EF effectively captures group-specific diversity, such as non-standard English, indigenous languages, and multilingual vocabularies. These findings indicate that adapting ML and NLP tools to properly handle such linguistic variation is essential to mitigate bias and marginalization. Although the lack of full-text limits structural analysis, EF remains a sustainable and highly useful resource for word-based research well beyond the HTRC&apos;s retirement.</dc:description>
      <dc:publisher>Dublin Core Metadata Initiative</dc:publisher>
      <dc:date>2026-09-10</dc:date>
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      <dc:identifier>https://doi.org/10.23106/dcmi.952651108</dc:identifier>
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      <dc:source>Dublin Core Metadata Initiative Conference Proceedings</dc:source>
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