Mindset and Guidelines: Insights to Enhance Collaborative, Campus-wide, Cross-sectoral Digital Humanities Initiatives
Bibliographic record
Abstract
At the heart of the emergence and development of the Digital Humanities has been the potential to move beyond the out-dated epistemological and metaphysical dichotomies of the later 20th century including quantitative-qualitative, pure-applied, and campus-community. Despite significant steps forward, this potential has been only partially realized as illustrated by DH pioneer Edward L. Ayers’ recent question, ‘Does Digital Scholarship have a future?’ As a way to think through current challenges and opportunities, this paper reflects on the building and initial use of the Canadian Century Research Infrastructure (CCRI). As one of the largest projects in the history of the social sciences and humanities, CCRI enables research on the making of modern Canada by offering complex databases that cover the first half of the twentieth century. Built by scholars from multiple disciplines from coast-to-coast and in collaboration with government agencies and the private sector, CCRI team members came to grips with key DH questions especially those faced by interdisciplinary, multi-institutional, cross-sectoral and internationally-connected initiatives. Thinking through this experience does not generate simple recipes or lessons-learned but does offer promising practices as well as new questions for future scholarly consideration.
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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 | 0.044 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".