When knowledges meet: Management and co-management of a declining salmon run in Subarctic Canada
Bibliographic record
Abstract
Dawson city is a small community located at the Northeast of Canada, just next to the Alaskan border. The city is most famous for the Klondike Gold rush that happened in 1896, however, indigenous people (in Canada called First Nations) have resided in the area for 10-15000 years. From the 1840s, non-First Nation people have arrived in Dawson, as miners, missionaries, adventurers, tourists and long-stay immigrants. Today, 2000 people live in Dawson, out of where 345 people are of First Nation origin, called the Tr ondëk Hwëch in. Next to Dawson flows the Yukon River. Every summer, two salmon species swim from the Bering Sea in Alaska to Dawson to spawn in the same creek they were once hatched. Harvesting salmon has been important to the Tr ondëk Hwëch in since the beginning of time. It is still of great importance to the Tr ondëk Hwëch in today, as food and as part of their culture and identity. The non-First Nation people in Dawson have since the 1840s engaged in the salmon fishery in different ways: as fishers, as consumers or as fish plant employees. Out of the two runs, the favoured species for human consumption has been the one species, Chinook salmon. The other, Chum salmon, has mostly been fished to feed dogs. From the 1990s, the Chinook salmon run started to decline. In 2013, the run was expected to reach an all time low. The Chinook fishery was restricted, allowing only people of First Nation origin to fish for Chinook salmon. In Dawson, views about salmon and salmon management differ whether a person belongs to the First Nation population, the non-First Nation population or is employed in the state bureaucracy. This thesis aim to investigate the different types of knowledges about salmon, asking the questions: What is a salmon? What is the proper relationship between humans and salmon? How should salmon be managed? The different knowledges have disparate relations to the processes of management and co-management of the Chinook salmon. The second half of the thesis aim to explore the meetings between the knowledges that occurred when people engaged in management and co-management. These meetings reveal structures of discursive power, as described by Michel Foucault (1980) and Eric Wolf (1989). Secondly, these meetings are examples of non-meetings, concerning the people who did not fit into co-management schemes and were not invited into the discussions and meetings regarding the management of Chinook salmon.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".