Digital Humanities as Inclusive Knowledge Translation: a Multi-Phase Qualitative Pilot Study
Bibliographic record
Abstract
Knowledge translation (KT), the dissemination of research outputs towards utilization and application, is increasingly recognized in research. For marginalized populations, benefiting from research outputs can be hindered by longstanding, inequitable access to information and education. The objective of this pilot study is to assess the potential of using creative works in the digital humanities - such as films, series, animations, games, and graphic novels - as knowledge translation tools for engagement, inclusivity, and equitable access to research-based knowledge. Methods followed a multi-phase process. First, an exploratory literature review was conducted on the intersection between three pillars: digital humanities, marginalized populations, and knowledge translation (Web of Science and Scopus), with 21 studies that met the inclusion criteria. Operational definitions and project framework (CATER) were drawn from the gap analysis, followed by a first round of pilot interviews with individuals with qualitative research experience. The first pilot interviews were conducted to identify any conceptualization errors and address methodological concerns. The second round of pilot interviews was conducted with marginalized individuals. Research findings show that marginalized populations access digital humanities for self-motivated learning. The implications of this research suggest digital humanities can serve as KT tools to supplement existing modes of KT, and that further participatory research will help uncover complex relationships between digital humanities and living with marginalization.
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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 | 0.035 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".