Reclaiming the Xhotsa: climate adaptation and ecosystem restoration via the return of Sumas Lake
Bibliographic record
Abstract
Sumas Lake (Xhotsa), located in the Fraser Valley, British Columbia, Canada, was the heart of Semá:th Nation Territory and the epicenter of a complex Indigenous food system. For the Semá:th people, the lake represented life and livelihood. In 1924, the lake was stolen and drained in an instance of land theft that occurred during a nationwide campaign of land dispossession and genocide, decimating an ecology that supported a rich and diverse Indigenous food system and replacing it with a settler food system. A century later, in November 2021 climate change induced flooding caused the lake to return, resulting in the evacuation of thousands of people and causing millions in damages to homes and infrastructure. Since the flood, the response has been a continuation of the status quo to protect settler agricultural lands via increased investment in hard structures that control the flow of water based on assumptions of the predictability of future flow conditions. We offer a missing narrative by bringing together an analysis of Indigenous laws and oral tradition with an assessment of the economic costs of “managed retreat”, defined as the purposeful relocation of people and infrastructure out of harm’s way. We find that the cost of buying out properties in the lakebed and allowing the lake to return is close to half the cost ($1 billion) of maintaining the status quo ($2.4 billion), while facilitating climate adaptation, and restoration of a floodplain ecosystem that supported thriving populations of people, salmon, sturgeon, ducks, and food and medicinal plants– including many species which are now endangered. Returning Sumas Lake by centering ‘Water Back’ as a climate resiliency solution, enacts both food systems and ecological reconciliation, addressing the harms caused by the loss of the lake to the Semá:th People that is still felt to this day. In a time when climate change induced flooding is predicted to increase, this study demonstrates how the inclusion of Indigenous laws and knowledges are critical to the development of solutions toward a more sustainable and just future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".