Understanding digital futures: Towards a framework for digital, community & youth engagement and research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper we offer an analytical framework exploring the extent to which rural communities are positioned to engage with digital technologies and some key factors influencing the process. Described as a ‘Digital Rural Research Framework’, it builds on the digital stages of readiness, capacity, use, and impact, and incorporates the community capitals literature to examine how different capitals can influence digital technology adoption. The paper outlines how the ‘Digital Rural Research Framework’ was applied to researching the experiences of young people in rural communities in Manitoba, Canada, using focus groups with rural youth and key informant interviews with rural leaders and partners involved in digital technology and programming across Canada. The authors apply the framework to explore both the barriers and opportunities of built, human, economic, social, cultural, and political capital related to digital technologies in rural communities. We conclude by emphasizing the importance of tailoring digital initiatives to address specific barriers and challenges in each rural area, and how the Digital Rural Research Framework can provide insights for place-based digital research and inform policy and practice. Overall, this research contributes to the understanding of the intersections between digital technologies and community capitals in rural contexts.
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.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it