Clio and Computers in Canada and Beyond: Contested Past, Promising Present, Uncertain Future
Bibliographic record
Abstract
Historians began using computers in the 1950s and 1960s when their possibilities seemed unlimited in the private, public, and non-profit sectors of wealthier countries. In this societal context, Clio met computers. In the following decades, a few historians would predict, from time to time, that digitally-enabled scholarship was on track to become the disciplinary norm. They emphasized the impact of specific initiatives enabled by changing technologies, from the mainframe era to microcomputers, the web, the tsunami of “born-digital” and digitized data, mobile devices, and new computational approaches such as machine learning. However, their predictions routinely failed to materialize and, while all historians might use digital tools at least to some extent, a claim that “we-are-all-digital-now” downplays substantive questions about History’s past and current relationship with new technologies. This article re-interprets the changing meaning of digital technologies within the disciplinary culture and institutional conditions of History. The evidence thus far reveals good reasons for both optimism and pessimism about digitally-enabled History at various times since the 1950s. By examining the complex and often surprising past and present, we can better determine and take the needed next steps in Digital History.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".