Teaching computational archival science: context, pedagogy, and future directions
Bibliographic record
Abstract
Introduction. The paper describes the development of the new transdisciplinary field of computational archival science (CAS) and the integration of computational thinking (CT) concepts into archival science. Method. The authors show how CAS can be introduced into graduate archival training through two case studies at the University of Maryland and the University of British Columbia and discuss building and sustaining CAS educator networks. Analysis. The paper argues that, given the increasing use of AI in archival work and research, the acquisition of computational skills and competencies is urgent for those entering the profession, but sees several barriers, including the willingness of archival educators to engage in this space, and the shortage of CAS educators. There is also a perceived conflict among some in the archival profession between CAS and recent archival scholarship emphasizing postcolonialism themes. Results. Results show this is a false dichotomy, as demonstrated by the many CAS papers focusing on ethical and social justice aspects of computing and archival work. Conclusion. The teaching of CAS is a necessity for archivists to stay relevant and responsive to the changing landscape. We offer CAS graduate curriculum learning guidelines, ensuring that archives remain accessible, trustworthy, and reflective of our evolving society.
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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 | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".