Increasing Access to Digital Archives Is a Complex Problem, and More Collaboration Between Archivists and Users Is Needed to Enact Solutions
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,037 | 0,086 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,006 | 0,010 |
| Études des sciences et des technologies | 0,013 | 0,018 |
| Communication savante | 0,033 | 0,060 |
| Science ouverte | 0,006 | 0,018 |
| Intégrité de la recherche | 0,007 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,022 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».