Indigenous Innovation: Exploring Substance Use And Drone Applications In Indigenous Contexts Across Canada
Bibliographic record
Abstract
This is a dissertation in practice which addresses Indigenous health, substance use, dronetechnology, and ethical considerations of drone use. A dissertation in practice results in three products with practical application in community. Through development of a grant application, journal manuscript, and community toolkit for ethical drone use, this dissertation addresses a broader narrative about the empowerment of Indigenous communities in Canada. In Chapter 2, a grant application has been designed for submission to Health Canada’s Substance Use and Addiction Program. The grant explores substance use among Indigenous Peoples working in the forest sector. The grant application addresses the need for Indigenous-led research and culturally sensitive assessment tools, and focuses on people with lived experience. In Chapter 3, the ethical implications of drone technology in health care delivery are explored through a qualitative study and journal manuscript. In the summer of 2024, eight semi-structured interviews were held with First Nation Peoples working in drone technology in Canada. Employing thematic analysis, 18 inductive codes were generated, which led to the construction of six themes: cultural sensitivity and inclusion, health care delivery and accessibility, ethical and legal considerations, education and community engagement, challenges and limitations, and future potential and recommendations. Chapter 4 builds off the manuscript to develop an evaluation framework and toolkit to align drone innovations in health care with Indigenous values and principles. Each of the six themes is described within the tool, and metrics or criteria for evaluating and scoring potential support or tension between community and industry are outlined. Most importantly, within the tool, the criteria and metrics guide mitigating identified tensions and enhancing alignment between the project and community needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.031 | 0.014 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".