Using Digital Technologies for Indigenous Sociocultural Advancement in an Era of AI: A Systematic Critical Synthesis
Bibliographic record
Abstract
Abstract Indigenous cultural resurgence parallels generative AI emergence. This article synthesizes digital technology projects for Indigenous sociocultural advancement. It analyzes 69 studies in five continents through a bifocal critical apparatus. The first lens uses activity theory to explore project ecologies comprised of peoples, objectives, places, technologies, and tensions. The second lens reveals ideopolitical framing patterns as studies are strategically positioned at the interface of Euro-Western and Indigenous cultures. Eight project types, developed by and for Indigenous peoples, are identified. Although consumer technologies predominate, many complex IT assemblages are attested. However, technological complexity often requires “outsider” experts, which limits local control over processes, data, and outcomes. The sampled studies highlight three ideopolitical frames: cultural bridging, countering Euro-Western dominance, and technical problem-solving. The foregrounded political themes are digital empowerment, data sovereignty, identity expression, and online activism. This study critically organizes underexplored research and charts new pathways for exploring digital technologies, culture, and Indigeneity.
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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 | 0.039 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".