Revisiting traditional land use and occupancy studies: Relevance and implications for resource management in Alberta
Bibliographic record
Abstract
This paper outlines the results of a study undertaken to assess the impact of Traditional Land Use and Occupancy Studies (TLUOS) on resource management policies and practices in the province of Alberta. The focus of this paper is policy-making bodies in industry and government; subsequent research is planned to explore the perceptions of First Nations' communities regarding the impact of TLUOS. Specifically, this study focussed on two TLUOS undertaken by two First Nations communities (Fort McKay First Nation and Dene Tha' First Nation) in partnership with the Arctic Institute of North America, and co-sponsored by industry and government. The paper situates the results of this particular study in a larger framework of current theory, understandings and discussions of First Nations' views of sustainable land use, traditional knowledge and collaboration. In analyzing the results from thirty interviews with academics and personnel from government agencies and industry, the researchers found that the application of the data from TLUOS was constrained by a number of interactive factors, both internal and external to the studies. Briefly, these include methodological and technical issues, accessibility of the data, the larger political context, the need for government leadership, differing expectations and agendas, and the need for a coordinated system for the conduct of TLUOS. In concluding, the authors note that in order for TLUOS to be effective in documenting traditional knowledge and influencing resource management policies and practices the following issues must be addressed: 1) the Alberta government should address the larger, unresolved issues between First Nations and the province; 2) appropriate mechanisms to blend traditional knowledge and scientific knowledge in resource management should be developed; and 3) a collaborative problem-solving process between First Nations, government and industry should be initiated by the Alberta government to address issues and create a coordinated system for the organization and implementation of TLUOS and their application to resource management policy and practice. Key words: traditional land use and occupancy studies, resource management, traditional knowledge, collaboration, sustainability, co-management, cooperative management, oil and gas industry, forest industry
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".