“Planning ahead is essential”: Discursive construction of Canadian national park landscapes and experiences
Bibliographic record
Abstract
,This inquiry explores the discursive mechanisms the Parks Canada Agency uses to construct national parks and visitors’ experiences of them. The objective of this study was to better understand the narratives that Canada’s national parks uphold and how this may impact the visitors that feel welcome in park landscapes. Historically, national parks were established through the expropriation and elimination of Indigenous Peoples from park lands so that colonial Euro-Canadian interpretations of the landscape, as empty wilderness, could be realized and preserved. Such understandings of national parks positioned humans and cultural history as separate from park landscapes and situate the wilderness as something humans can venture into to endure or overcome for personal virtue. Recently, Parks Canada shifted its approach to park management, now recognizing that humans and the environment are inseparable, and that natural and cultural heritage are necessarily enmeshed. Further, the Agency is making specific efforts to welcome diverse populations into national parks that wilderness narratives have historically excluded. Through a discourse analysis of over 400 Parks Canada publications from three case parks – Banff National Park, Rouge National Urban Park, and Kluane National Park and Reserve – this inquiry interrogates how this new understanding aligns or clashes with traditional wilderness narratives in the materials that visitors use to plan their national park experiences. This thesis argues that despite some progress, wilderness narratives still dominate Parks Canada publications, and the visitation practices the Agency supports. Similarly, the information available to visitors offers limited guidance on the structural practices of park visitation, thus making it more difficult for inexperienced visitors to access national parks.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.035 | 0.040 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 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".