Negotiating the Political Ecology of Aboriginal Resource Management: How Mi'kmaq Manage Their Moose and Lobster Harvest in Unama'ki, Nova Scotia, Canada
Bibliographic record
Abstract
Since the Supreme Court of Canada affirmed the fishing and hunting rights of the Mi'kmaq nation in 1985 and 1990, the government has failed to accommodate these in appropriate and effective resource management frameworks. In\nUnama'ki/Cape Breton Island, Nova Scotia, the subsistence harvest of lobster and moose by Mi'kmaq has therefore caused cross-cultural conflict and ecological concerns. Since 2006, the Lobster Management Plan (Unama'kik Jakejue'ka'timk) and the Moose Management Plan are being developed under Mi'kmaq leadership to manage the Mi'kmaq harvest communally.\nThese innovative management initiatives will serve as case studies for this thesis to explore how Mi'kmaq negotiate the political ecology of co-management in Nova Scotia and effectively assert Mi'kmaq rights to resource harvest and selfgovernance. Most notably, the management plans employ cultural principles of sustainability and pro-active approaches to cross-cultural communication. This research shows how Mi'kmaq communities have developed resource management capacities and frameworks that can also inspire the self-government aspirations of\nother aboriginal nations in Canada. Mi'kmaq strategies and experience suggests that\naboriginal leadership and cultural principles are integral to the meaningful implementation of aboriginal resource rights.\nSemi-structured interviews with Mi'kmaq and governmental resource managers illustrated diverse discourses of aboriginal resource rights, ecological knowledge and sustainability. Aiming to represent research insights appropriately,\nthis thesis follows the decolonization agenda of aboriginal methodologies and features reflective discussions of the author's positionality within the Mi'kmaq research community. This also allows for a review of how the author came to terms\nwith conflicting discourses and aboriginal ontologies of ecological knowledge, as\nwell as the requirements for decolonizing research.\nSupporting reflective insights, a framework of anthropological political ecology and poststructuralist arguments for ontological diversity explain the validity\nof aboriginal perspectives on ecological knowledge and resource rights, which is the\npremise of decolonization paradigms. A review of engaging with aboriginal culture\nboth in theory and practice concludes that the practical experience is essential for an\nappreciation of aboriginal perspectives and thus integral to cross-cultural communication and co-management relationships.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".