Reconciliation and Renewed Relationships in the Co-management of National Parks
Bibliographic record
Abstract
A new era of Indigenous-led collaborations signals a shift in approach by Parks Canada – in response to commitments to reconciliation – to the involvement of Indigenous peoples in the governance and management of national parks, national park reserves, and national marine conservation areas. However, co-management, the institutional arrangement on which these and other longstanding partnerships in parks contexts have been built, has a contested and uneven track record in meeting the needs, interests, and aspirations of Indigenous people. Using qualitative methods of governance analysis combined with interviews reflecting Indigenous and non-Indigenous perspectives, this thesis addresses the question: “what is the potential of co-management as a vehicle for reconciliation within national parks”? The thesis is comprised of two manuscripts. The first confronts a critical gap in empirical data about the content and context of formal national park co-management agreements through a scan of available agreements and the creation of a governance typology, as a basis for exploring strengths and weaknesses of agreement-making in serving reconciliation commitments. The second, through a community-partnered project with Vuntut Gwitchin First Nation, examines relationship-building processes in the context of Vuntut National Park, as an example of a mature claims-based northern national park co-management arrangement. Using the lens of ethical space, the research sheds light on enabling and constraining factors for relationship-building and offers insights into the principles and elements of an ethical space process for national park co-management arrangements supportive of Indigenous-state reconciliation. Overall, this thesis aims to contribute to understandings of the potential of co-management agreements to support reconciliation and renewed relationships in 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 | 0.036 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.050 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.002 | 0.005 |
| 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".