Developing an e-Community Approach to Community Services in Kitigan Zibi Anishinabeg First Nation
Bibliographic record
Abstract
Kitigan Zibi Anishinabeg First Nation is a leader in community and social services. This rural First Nation – the largest Algonquin community in Canada - has since 1980 successfully supported community members to take ownership of service development and delivery. They have made many services and programs available to community members, including: an elementary and secondary school, a day-care, a community hall, a community radio, a health centre, a police department, a youth centre, and others. Their community services are led and staffed by fully trained and qualified community members. As computers, broadband internet and cellular services have become available in Kitigan Zibi, the service sectors have been integrating these technologies with a goal of improving services for and communications with community members. However they face many challenges in their efforts to remain innovative and plan for future delivery of services using technologies. Our study, based on qualitative analysis from interviews with 14 community services staff in Kitigan Zibi, will explore their current successes, challenges, and future potential for integrating information and communication technologies (ICT) into services that promote community and social development. The analysis discusses the eCommunity approach advocated by the Assembly of First Nations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".