Weaving Indigenous Digital Strategies from Alberta, Canada and Oaxaca, Mexico
Bibliographic record
Abstract
The mainstream history of the Internet, digital information and communication technologies (ICTs) shows a clear shift to a centralized digital landscape that is predominantly controlled by corporations. However, diverse individuals, communities and Nations around the world have created connectivity and communication models that respond to their own needs and contexts. For example, Indigenous Peoples from different territories, who have resisted colonial structures and demonstrated the sustainability of their communities, organizations, and Nations in relation to their territories, have developed and used ICTs to support their own ways of living. In this context, my research explores the strategies that Indigenous artists and communicators from territories in Mexico and Canada are undertaking to use digital tools according to their own terms and desires. Through testimonio as a narrative research methodology, this work assembles, shares and reflects on the journeys of Ayuujk, Xhdiza, Zapoteco, Nehiyaw, Dene and Métis artists and communicators from territories within the regions of Oaxaca and Alberta, in relation to the interconnected digital landscape of what we call the Internet. This thesis is informed by a body of research that acknowledges that digital ICTs are not neutral, but tools of power and counterpower. The testimonio stories presented here provide a variety of experiences and reflections that reveal the strategies that Indigenous artists and communicators have used when interacting with digital tools, such as critical and reflective processes, experiences informed and nurtured by webs of relationships, relation to Land and Language and collective dreaming. Presented as first person narratives, these stories speak of resistance, memory, language revitalization, and collective processes. They reflect journeys that involve digital tools such as video game designing, radio broadcasting, app designing, online streaming and gaming, digital collaging and filmmaking. Moreover, these testimonios provide insights about the role of non-Indigenous allies to contribute to their ideation of a digital world. In presenting this research, I describe my own self-reflective journey that seeks to contribute to more appropriate, coherent and ethical collaborations in both research and technology, through relational and reflective methods and processes while honouring people's stories.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".