Disruption of Academic Archival Practice: A Preliminary Examination of Finding Aids
Bibliographic record
Abstract
The purpose of this study is to investigate researcher use of finding aids to access archival holdings. The investigators examined a set of institutional data from Archives and Special Collections at Western University, a Canadian academic archives, to determine how users interact with finding aids which are available via the institutional website. Creation of finding aids is a long-standing part of archival practice and in fact finding aids have traditionally been perceived as the primary tool used to access archival holdings. However, technology has brought forward several new ways of creating and providing access to descriptive data—for example, online public access catalogues with keyword searching. This research project, which builds on previous research in this area, explores the idea that the traditional tool of finding aids may not be meeting users’ needs. Preliminary analysis reveals that some researchers do interact with finding aids whereas other researchers prefer to email the archives directly to ask for assistance. User needs are complex and the traditional structure and presentation of finding aids may not be meeting these needs. Archivists need to conduct more in-depth research into user experience and must disrupt academic archival practice by revisiting the format and presentation of finding aids to meet evolving user needs.
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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 | 0.032 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".