“This Land was Your Land” Indigenous Engagement and Partnership in the Canadian Outdoor Recreational Landscape
Bibliographic record
Abstract
Recreating outdoors is something that many people in Canada enjoy doing and often do so in National Parks or nature close to their homes, which Canada has an abundance of. However, recreation is a heavily colonized field that often excludes Indigenous people, who have been using the same land for numerous years. This thesis focuses on bringing Indigeneity back into the outdoor recreational landscape through engagement and partnerships. It also looks at how users can contribute to the reconciliation and decolonization of the outdoor recreational landscape. The ongoing National Urban Park initiative within the city of Edmonton is looked at as a developing case. A dive into the background of this topic provides an extensive overview of the problem. This research therefore discusses how engagement and partnership can bring back The qualitative empirical data that comes from semi-structured interviews and online media analysis, using content analysis to identify underlying themes. The concept of the Ethical Space serves as the theoretical framework that encompasses the research. Ethical space, as defined by [theoretical source], is a space where Indigenous and non-Indigenous knowledge systems can coexist and interact in a mutually respectful and beneficial manner. It provides a framework for working with Indigenous communities from start to end, and maintaining meaningful engagement practices and relationships with them, even after the partnership or project is completed. Two analysis processes have been looked at. First, engaging and partnering with Indigenous communities and second, reconciling and decolonizing the outdoor recreational landscape. Within these two topics, the summary of findings discusses (1) engagement, partnerships, and relationship building, (2) best and poor practices, (3) decolonization and reconciliation, and (4) the ethical space. Learning from mistakes and committing to do better are ways to do so, and can deconstruct systematic racism and power imbalances, and promote ethical and meaningful transcultural interactions.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.018 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".