Promoting Indigenous Culture Using AI Algorithms on Social Media: Effective Strategies for Improving Mental Health among Canadian Youths
Bibliographic record
Abstract
This study explores the intersection of culture, technology, and mental health in the digital age, focusing on the impact of promoting Indigenous culture on social media to support Canadian youths' psychological well-being. Recognizing the historical marginalization of Indigenous narratives, it investigates how AI algorithms integrated into social media platforms enhance the dissemination and engagement of cultural content. A controlled experiment was conducted where two groups of youths engaged with either a traditional or AI-powered platform promoting Indigenous culture. Over five days, their mental health and cultural engagement were assessed. Results revealed that the AI-powered platform significantly improved participants' cultural identity, sense of belonging, and mental health compared to the traditional platform. These findings suggest that AI technology can play a transformative role in delivering culturally relevant content, offering a promising approach to addressing mental health disparities in Indigenous communities and beyond. The study contributes to understanding how AI can enhance digital cultural promotion and support mental well-being.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".