Bibliographic record
Abstract
Drones will revolutionize various aspects of Canadian society. Medical cargo drones are transporting crucial supplies and biological samples, such as blood plasma and organs. This article explores Indigenous ethical dimensions of integrating drones in Canadian contexts, represented by the acronym DRONE that embodies the following key principles: D: Decolonize—This principle advocates for methodologies that aim to rectify historical injustices and align research with Indigenous customs and storytelling. R: Respect, Reciprocity, Relationship, and Relevance—These principles emphasize equity, mutual respect, and relationship-based collaboration in drone technology. O: Ownership, Control, Access, and Possession—OCAP® recognizes Indigenous self-determination in research and development projects, focusing on data ownership and control. N: Natural Law—This principle underscores the importance of respecting the environment and harmonious relations between Indigenous communities and the natural world in drone projects. E: Economic Development—Acknowledging the significance of Indigenous economies and addressing historical financial barriers, the drone industry can contribute to economic prosperity in Indigenous communities. These principles are an ethical imperative to fostering trust in Indigenous communities. Partnerships guided by the DRONE framework facilitate culturally sensitive, ethically sound, and effective solutions, advancing inclusivity and responsible technological innovation.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.020 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".