Understanding digital futures: Towards a framework for digital, community & youth engagement and research
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.
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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 | 0.019 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.012 | 0.049 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".