Developing Academic Capacity in Digital Humanities: Thoughts fromthe Canadian Community
Bibliographic record
Abstract
Despite DH’s long history, it is still perceived as a relatively emergent academic disciplinewhich has several implications for its ongoing development and acceptance. In order tounderstand its role in supporting the field’s development and acceptance, SSHRCcommissioned a survey of the larger Humanities and Social Science’s community tounderstand the issues related to DH’s development and acceptance and the types ofactivities that should be funded. The survey results suggest there is reason for optimismregarding the growing acceptance of digital methods, resources and tools and electronicdissemination as instructors, researchers, and students are using and publishing indigital outlets and creating and employing digital recourses, methods and toolsandventuring into new research fields. This trend is likely to continue as students andyounger scholars continue to embrace the digital in all aspects of their personal andprofessional lives. However, this optimism should be tempered to some extent asstudents and junior faculty are still less likely than associate professors to present andpublish their digital-oriented research for a variety of reasons. The field’s more seniorfaculty can mentor their junior colleagues and students to this end and shape salary,tenure and promotion policies to recognize and reward these efforts. Finally, issuesremain around the amount of funding required for the initial development and ongoingsustainability and relevance of digital resources and may become more critical over time.Granting agencies will need to evaluate their funding role in this regard.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.073 | 0.033 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".