An argument for ethical physical geography research on Indigenous landscapes in Canada
Bibliographic record
Abstract
Abstract In Canadian physical geography, the ethical implications of research occurring in Indigenous spaces and places have historically been overlooked. Physical geographers, particularly those working in northern Canada, are beginning to recognize that our research takes place in a sensitive social space and the knowledge we pursue has ethical and moral implications. The Canadian Geographer recently published a special issue (56:2) that documents the many challenges and opportunities of community‐based participatory research involving Indigenous peoples in Canada. Throughout that issue, the 2010 Tri‐Council Policy Statement, Ethical Conduct for Research Involving Humans (TCPS2), was referenced as important in directing a shift towards ethical interactions with Indigenous peoples in research. Drawing on material from the special issue and the TCPS2, this article gives an overview of the authors' experiences in attempting to execute an ethically sound physical geography study in traditional Dene territory in northern Saskatchewan. The viewpoint concludes with thoughts on what bridges and barriers exist when attempting physical geography research that is sensitive to the ethical responsibilities of working in Indigenous spaces. From our perspective, physical geographers can strengthen the ethical defensibility and overall quality of their research by enhancing involvement with indigenous communities that are potentially impacted by their research findings.
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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 | 0.075 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.069 | 0.126 |
| Scholarly communication | 0.025 | 0.009 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.013 | 0.024 |
| 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".