Resource Management and Reconciliation: Co-management for conflict reduction
Bibliographic record
Abstract
In North Bay, Ontario, Lake Nipissing walleye exist in a state of crisis. Walleye are a popular target for Indigenous and non-Indigenous people alike; the Nipissing First Nation (NFN) exercise their treaty right to commercially fish in Lake Nipissing, alongside non-Indigenous fishers regulated by Ontario’s Ministry of Natural Resources and Forestry (MNRF). Over the past several decades, conflict has developed between these groups over unequal access to the declining common resource, and resource management challenges have arisen where the number of fish taken from the population is unknown. Northern Ontario moose are in a strikingly similar position. In this paper, I explore the complex interaction of socio-cultural, political, and legal factors implicated in conflicts between Indigenous and non-Indigenous interest groups over declining common resources in northern Ontario. In Part I, I consider and reject the current approach to resource management comprised of MNRF regulation and colonial jurisprudential understanding of treaty rights and reconciliation. Next, I discuss in detail the socio-cultural manifestations of local and regional conflicts over Lake Nipissing walleye and northern Ontario moose. As a foundation for my proposal of an improved approach to resource management, in Part III, I establish Indigenous jurisdiction over environmental matters as a function of Indigenous law – explicitly rejecting Canadian law as a basis for this jurisdiction. Moreover, I recast the notion of “reconciliation” as an exercise in understanding Indigenous interests with reference to Indigenous philosophical traditions and disrupting assertions of Crown sovereignty to recognize Indigenous self-governance. Finally, in Part IV, I propose a set of recommendations for an improved approach to resource management, based on an adaptive co-management model.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.022 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".