Managing nature, producing cultures : Inuit participation, science and policy in wildlife governance in the Nunavut Territory, Canada
Bibliographic record
Abstract
In this thesis, a critical analysis is proposed of the relationships between Inuit participation, science and policy in wildlife governance in the Nunavut Territory, Canada. This analysis situates the emergence of a participatory regime for the governance of wildlife in Nunavut, explores its performance and examines the relations between the ways in which wildlife governance arrangements are currently represented in policy and how they are played out in practice across the territory. To pursue these objectives, this research draws upon a number of theoretical perspectives and methodological strategies poised at a crossroads between environmental geography, science and technology studies, political ecology and ecological anthropology. It combines participant observation, semi-directed interviews and literature-based searches with approaches to the study of actor-networks, hybrid forums and scientific practices associated with Latour and Callon, as well as with Foucauldian and post-Foucauldian analyses of power, governmentality and subjectivity. This analysis suggests that the overall rationale within which wildlife governance operates in Nunavut remains largely based on a scientific and bureaucratic framework of resource management that poses significant barriers to the meaningful inclusion of Inuit views. In spite of their participation in wildlife governance through a range of institutional arrangements, consultation practices and research initiatives, the Inuit of Nunavut remain critical of the power relations embedded within existing schemes, where significant decision-making authority remains under the control of the territorial (or federal) government, and where asymmetries persist with regard to the capacity of various actors to produce and mediate their claims. In addition, while the use of Inuit knowledge, or Inuit Qaujimajatuqangit, in wildlife governance in Nunavut has produced some collaborative research and management endeavours, it has also crystallised a divide between ‘Inuit’ and ‘scientific’ knowledge, generated unresolved conflicts, fuelled mistrust among wildlife co-management partners and led to an overall limited inclusion of Inuit observations, values and beliefs in decision-making.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.025 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".