Future Horizons: Canadian Digital Humanities
Bibliographic record
Abstract
Across more than twenty chapters, Future Horizons explores the past, present, and future of digital humanities research, teaching, and experimentation in Canada. Bringing together work by established and emerging scholars, this collection presents contemporary initiatives in digital humanities alongside a reassessment of the field’s legacy to date and conversations about its future potential. It also offers a historical view of the important, yet largely unknown, digital projects in Canada. Future Horizons offers deep dives into projects that enlist a diverse range of approaches—from digital games to makerspaces, sound archives to born-digital poetry, visual arts to digital textual analysis—and that work with both historical and contemporary Canadian materials. The essays demonstrate how these diverse approaches challenge disciplinary knowledge by enabling humanities researchers to ask new questions. The collection challenges the idea that there is either a single definition of digital humanities or a collective national identity. By looking to digital engagements with race, Indigeneity, gender, and sexuality—not to mention history, poetry, and nationhood—this volume expands what it means to work at the intersection of digital humanities and humanities in Canada today. Available formats: trade paperback, accessible PDF, and accessible ePub
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.059 | 0.006 |
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".