An Inquiry into Community Members’ Use and Attitudes toward Technology in Mishkeegogamang Tepacimowin Networks
Bibliographic record
Abstract
Mishkeegogamang First Nation is a rural Ojibway community in Northwestern Ontario. Mishkeegogamang community members of all ages use a wide array of information and communication technologies (ICT) as tools in daily life, and as a means to support individual and community goals. This collaborative paper tells the story of how Mishkeegogamang uses ICT for community development, drawing on 17 interviews with community members, and several community member profiles. A basic descriptive quantitative analysis is also provided, giving information on frequency of use of a wide variety of technologies. Community informatics theory guides the interpretation of the findings. A broad range of ICT use by community members will be explored, including the Mishkeegogamang website, the busy yet invisible use of social networking sites, youth and ICT, ICT for health and education, and ICT to support traditional activities. Finally, a section on challenges and needs for facilitating ICT use is also provided. Mishkeegogamang has collaborated on a rich chronicle of its land and people in the Mishkeegogamang book: The Land, the People, and the Purpose (Heinrichs, Hiebert, & The People of Mishkeegogamang, 2009). This paper is conceptualised as a new chapter, documenting how community members use ICT in their daily lives and for community development. There have been no similar past explorations that have addressed this area. In addition, within the broader literature on First Nations in Canada, there have been few to no published accounts of community members’ perspectives and uses of ICT. This study is part of a broader collaborative research project called (First Nations Innovation), which explores how remote and rural First Nations are using information and communication technologies for community development.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".