Resilient e-community initiatives and partnerships in remote and rural First Nations
Bibliographic record
Abstract
Canada’s First Nation leaders have adopted the e-community approach for their local broadband development. Services supported within the e-Community include e-health, e-learning, e-business and e-work as well as supporting community members’ activities ranging from online banking to networking with social media. The chiefs directed their national organization, the Assembly of First Nations, to drive the e-community development at all levels of government (Whiteduck, 2010). E-community is fueled by the desire of First Nations to own, control, and manage their local infrastructure and online services. Community infrastructure has been identified as an indicator of community resilience (Kimayer, 2009). This presentation highlights the importance of locally owned and managed telecommunication infrastructure supporting First Nation e-community and local resilience. E-community provides choices for local people to remain in their communities and contribute to the growth and positive development in these challenging environments. First Nations organizations are developing comprehensive e-community initiatives in collaboration with their member First Nations as intermediary delivery agents. My presentation highlights how local broadband infrastructure is supporting community resilience through social and economic developments. The research findings provide evidence for national and regional programs to support First Nation ownership, control and management of local infrastructure and broadband-enabled services.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".