Archives in the Life of the User: What Archives Can Learn from User-Centric Museums
Bibliographic record
Abstract
Library, archive, and museum convergence is still a topic of much contention in some academic circles. However, resistance is perhaps most entrenched in the archival discipline. This article attempts to briefly examine why that might be the case, and then asks the question: What can archives learn from museums' relatively-new increased focus on being user-centric? The author uses the extraordinary scholarship of authors such as Paul F. Marty, W. Boyd Rayward, and numerous others in order to examine changes in libraries and, most specifically, museums, as well as the creation of the museum information professional role and the use of museum informatics. From this examination, the article suggests that archives and archivists could indeed benefit greatly from further explorating into, and adaptation of, the museum world's increasingly user-centric approach. It is furthermore suggested, following in the prior steps of libraries and museums, that the focus of archives should move from a "user in the life of the archive" to an "archive in the life of the user" mentality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.043 |
| Scholarly communication | 0.039 | 0.060 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".