Indigenous Principles of Wild Harvest and Management: An Ojibway Community as a Case Study
Bibliographic record
Abstract
In colonial nations such as Canada, there have been increasing requirements for governments to engage directly with Indigenous communities regarding their rights and interests in natural resource management generally, with specific focus on the role of Indigenous knowledge systems in harvest management decision-making (Tikina et al. 2010 ). Canadian courts have repeatedly focused on two factors with extremely important consequences for the Nation-to-Nation relationships that exist between the Crown and the Indigenous communities: (1) Indigenous rights must be reconciled with other government responsibilities including justified infringements for the often ill-defined concept of ‘conservation’ (Crawford and Morito 1997 ; Ayers 2005 ; Nadasdy 2005 ), and (2) the ‘honour of the Crown’ must be maintained when consulting Indigenous communities, especially with regard to management decision-making about their natural resources (Morito 1999 ; Slattery 2005 ). Given the legal necessities for a Eurocentric government to engage in honorable and meaningful consultation with Indigenous communities about conservation ethics and natural resource management, it remains to be seen how these Indigenous-Western science cross-cultural consultations should be undertaken (Crawford et al. 2010 ). The trend to date has largely been the domination of Western Science over Indigenous knowledge systems (in the sense described by Pentland 1995 ); circumstances in which Indigenous knowledge holders might be requested to provide information to scientists/managers who would evaluate it for reliability and utility before deciding whether to incorporate in a science-based management program (McGregor 2004 ; Clark and Slocombe 2009 ; Lyver et al. 2009 ). Some scholars have suggested that conflict caused by this kind of cultural domination could be reduced if governments and Indigenous communities re-initiated their discussions with an examination of similarities and differences in principles regarding ‘conservation’ and ‘natural resource management’ (Ratner and Holen 2007 ; Ebbin 2011 ; Watson et al. 2011 ). In this way, the communities could develop a structured and respectful dialogue about wild harvest management in the spirit of reconciliation and productive collaboration.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.044 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".