Increasing Access to Digital Archives Is a Complex Problem, and More Collaboration Between Archivists and Users Is Needed to Enact Solutions
Bibliographic record
Abstract
A Review of: Jaillant, L. (2022). How can we make born-digital and digitised archives more accessible? Identifying obstacles and solutions. Archival Science, 22, 417-436. https://doi.org/10.1007/s10502-022-09390-7 Objective – To outline current levels of access to digitized and born-digital collections, investigate and identify obstacles to increasing access, and suggest possible solutions. Design – Semi-Structured online interviews. Setting – Archives, libraries, and museums based in the UK, Ireland, and the United States. Subjects – A total of 26 practitioners in archives, libraries, and museums including 12 women and 14 men. Methods – The researchers recruited participants from existing personal contacts and those contacts’ colleagues, with attention toward diversifying in the areas of gender, career stage, institution size, and geographical location. Twelve interview questions were sent to interviewees in advance, but the questions were tailored to each interviewee during the interview with follow-up questions asked as necessary. A team of three Digital Humanities scholars conducted 21 interviews with the 26 subjects, and all but three interviewees agreed to be named in the resulting article. All interviews were conducted in May 2021, except one, which was conducted in November 2020. Main Results – The author discusses relevant paraphrases and quotations from the interviewees under four headings: “Obstacles to access to digitised collections,” “Born-digital collections: from creation to access,” “Current levels of access to digital collections,” and “Possible solutions to the problems of access.” Key obstacles to access that emerge throughout the discussion include technological obsolescence, copyright and permissions, data protection of sensitive materials, lack of a market for born-digital records, and the problem of scale and skill gaps. Strategies to increase access include enhanced collections, less restrictive legislation, new access interfaces including virtual reading room software, use of artificial intelligence to increase discoverability, and web archives. The author makes distinctions between born-digital (e.g., emails) and digitized (e.g., scanned photographs) content throughout the discussion of results. Conclusion – There is a paradox between the focus on data analysis in current research and the difficulty researchers have in accessing cultural data through digital archives, but increasing access to digital collections remains a challenging and complex problem. The author highlights some possible solutions that emerged from the interviews, including artificial intelligence, but also emphasizes the need to bring together an interdisciplinary community of both archivists and users, to continue shifting the conversation surrounding digital collections from focusing on preservation to focusing on access, and to advocate for changes to legislation, digitization practices, and copyright clearance.
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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 | 0.037 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.033 | 0.060 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".