Indigenizing Digital Literacies: Community Informatics Research with the Algonquin First Nations of Timiskaming and Long Point
Bibliographic record
Abstract
Community-engaged digital literacies initiatives can greatly benefit from knowledge and practices developed by Indigenous peoples. In this paper, we describe a research project to develop digital literacies with two Algonquin First Nations in Quebec: Timiskaming and Long Point. This project reflects a First Mile approach to Community Informatics, informed by the theoretical framework of Indigenous resurgence and by engaged research methodologies. In telecommunications and broadband terminology, communities are typically framed as the ‘last mile’ of development. The First Mile approach challenges this situation by encouraging projects that emerge from the locally determined needs of collaborating communities, who gain ownership and control of processes and outcomes. Drawing on community-engaged research methodologies, university-based researchers facilitate this work while community-based researchers integrate data collection, analysis, and public outreach activities into the lived realities of community members. We discuss how digital literacies projects can benefit from the theoretical framework of Indigenous resurgence, which stresses the daily practices that support the continual renewal of Indigenous communities.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".