Towards Biocultural Conservation: Local and Indigenous Knowledge, Cultural Values and Governance of the White Sturgeon (Canada)
Bibliographic record
Abstract
This paper examines the extent to which Indigenous knowledge and values have informed conservation of the Lower Fraser River population of white sturgeon (Acipenser transmontanus) in Canada. A review of grey literature and semi-structured interviews carried out with indigenous Stó:lō fishers and fisheries managers in the Lower Fraser Basin in 2016–2018 evidences the depth of knowledge held by Stó:lō fishers about this species and its importance to local communities. A summary of Stó:lō oral histories about the sturgeon and observations and experiences of settlement and development in the Fraser region, provides context for understanding why and how the white sturgeon came to be listed as a species at risk. However, the impacts were not only ecological; Stó:lō people were also significantly impacted by European settlement and development of the Fraser Basin over the last one hundred years. The assessment of the white sturgeon, under the Canadian Species at Risk Act in 2012 was a missed opportunity to decolonize current management approaches. The paper concludes by suggesting that a biocultural diversity conservation approach, that reflects both ecological and socio-cultural values, and is informed by scientific and Indigenous knowledge systems, is a more sustainable approach to the management of the white sturgeon and other species at risk.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".