Catching The Train? A Community-based Analysis of Digital Technologies Utilization in Rural Manitoba, Canada
Bibliographic record
Abstract
Despite new opportunities for development in rural communities, digital technologies have not always met expectations. In particular, their status as a panacea for rural communities has been extensively discussed over the last twenty years. Many rural communities have been slow to adopt these technologies or achieve meaningful outcomes, further contributing to the urban-rural digital divide. Researchers have identified the use of digital technologies as an important area requiring better understanding in order to realize their potential for expanding and developing capitals. In this presentation, we investigate the opportunities and barriers associated with the use of digital technologies in three rural communities of Southern Manitoba, Canada. The content of interviews with business representatives and focus groups with youths and seniors is analyzed through the theoretical framework of the community capitals (CC). In our operationalization of the CC framework, we introduce a dynamic perspective by discussing capitals interactions as well as in- and out-flows of capitals. The analysis highlights the resilience of rural communities and the creativity associated with their utilization of digital technologies, which enable them to take advantage of opportunities despite serious barriers. In particular, preliminary results show that: - The lack of infrastructure to support coverage and connectivity can lead to security issues; - Access costs are higher in rural areas, where extra equipment is needed to boost signals and services are not always delivered; - Skills are mostly self-taught, which fits the entrepreneurial attitude of most business representatives but can result in fears among seniors who tend to learn more slowly; - Integration of digital technologies is fast-paced in every domain of everyday life (education, social life, economic activity)
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".