The Archive and the Digital Age: Field Notes from the Pedagogical Front
Bibliographic record
Abstract
The digital environment in which the humanities are now firmly immersed has opened the door to innovative ways for students to interact with traditional formats such as archival and print material, and to develop a deep and personal understanding of topics and issues. Libraries, museums and archives are in the unique position of facilitating the creation of digital initiatives in the classroom by offering up their collections as “learning laboratories,” and by sharing their expertise in technology, information, and digital literacy as well as data management. Through active collaboration with course instructors, they can build bridges between their collections and the digital skills students need in order to embrace the new learning paradigm and to help lead them into the future. This paper outlines an archival-digital pilot launched in 2015 at the University of Ottawa, Canada. It situates the project in its historical context; details its early and subsequent iterations; and surveys the assumptions, challenges, surprises, and pleasures of introducing students to archival sources and to acquiring digital skills.
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.
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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.027 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".