Co-management as an Ethical Space of Engagement: Prospects for Reconciliation in Vuntut National Park
Bibliographic record
Abstract
Parks Canada’s commitments to reconciliation marks a significant shift in the governing paradigm of national parks, moving away from a colonial framework towards models that respect and elevate Indigenous forms of governance and knowledge systems. However, the extent to which a land claims–based co-management model, as the dominant mechanism of governance and engagement employed by Parks Canada, can serve as a vehicle for reconciliation is an open question. This paper endeavours to better understand the relational dynamics of co-management for their potential to advance reconciliation in national park settings. Using the lens of ethical space, we identify factors that either facilitate or impede relationship building between Vuntut Gwitchin First Nation and Parks Canada in the co-management of Vuntut National Park. Insights from semi-structured interviews conducted in 2021 with 11 community members and park managers indicate that an evolving-management relationship is sustained through extensive community engagement and Parks Canada’s support for strengthening community connections to the land. By adopting an ethical space framework for analysis, we were able to highlight different conditions and elements necessary for a protected-area co-management arrangement to serve as a solid foundation for reconciliation. Our analysis also revealed various structural impediments to the establishment of an ethical space conducive to reconciliation, particularly in the context of co-management arrangements based on land-claims agreements. These challenges included: discrepancies in approaches to protected-area management between Vuntut Gwitchin First Nation and Parks Canada, further complicated by the governance system employed by Parks Canada; issues of scale related to Parks Canada’s nested management structure, which affected relationship building between co-managers; financial constraints; and capacity constraints.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".