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Archively

Research

Research Notes

Long-form research for directors of digital collections and working archivists. Each note examines one question in machine-assisted archival description, cites archival standards and professional guidance, and identifies the author's own proposals. Every note is versioned and includes an address for corrections. Read on the web or as a PDF, without signing up.

Notes published
1
Reading time
28 min
Figures
4
Format
Web and PDF, no sign-up

Archively Research Note 01

When Does an AI Proposal Become Archival Description?

A human reviewer is necessary, but not sufficient. Professional control depends on who may accept a machine proposal, what context and criteria they use, what choices they can actually make, and what evidence and correction path the institution keeps afterwards.

Machine systems now produce text that looks like archival description: transcripts, candidate dates, titles, scope and content notes, subject terms, biographical statements.

Saad Muhammad, 28 min read, 4 figures, version 1.0, , PDF edition

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