Archival Records and Training in the Age of Big Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Purpose – For decades, archivists have been appraising, preserving, and providing access to digital records by using archival theories and methods developed for paper records. However, production and consumption of digital records are informed by social and industrial trends and by computer and data methods that show little or no connection to archival methods. The purpose of this chapter is to reexamine the theories and methods that dominate records practices. The authors believe that this situation calls for a formal articulation of a new transdiscipline, which they call computational archival science (CAS). Design/Methodology/Approach – After making a case for CAS, the authors present motivating case studies: (1) evolutionary prototyping and computational linguistics; (2) graph analytics, digital humanities, and archival representation; (3) computational finding aids; (4) digital curation; (5) public engagement with (archival) content; (6) authenticity; (7) confluences between archival theory and computational methods: cyberinfrastructure and the records continuum; and (8) spatial and temporal analytics. Findings – Each case study includes suggestions for incorporating CAS into Master of Library Science (MLS) education in order to better address the needs of today’s MLS graduates looking to employ “traditional” archival principles in conjunction with computational methods. A CAS agenda will require transdisciplinary iSchools and extensive hands-on experience working with cyberinfrastructure to implement archival functions. Originality/Value – We expect that archival practice will benefit from the development of new tools and techniques that support records and archives professionals in managing and preserving records at scale and that, conversely, computational science will benefit from the consideration and application of archival principles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it