Revisioning Fire : A study on cultural burning and fire settlement planning
Bibliographic record
Abstract
In this thesis, I examine the impact fires have had on Canadian communities as a result of climate change and discuss a new form of settlement design that addresses this crisis and offers an efficient solution to community displacement. The history of Canada has marked a departure from and erasure of Indigenous teachings about the regenerative properties of fire and the benefits of working with fire to promote healthier land. Instead, the prevalent mindset extensively deals with fire as something destructive that should be avoided and prevented in all cases. Shifting Canada’s outlook back towards the practice of entertaining fire’s regenerative properties offers a solution to the devastation of wildfires. The proposed new form of settlement design will employ elements of current fire-management design, cultural burning, and a study of local plants’ relationships with fire to help future wildfire prevention and response within the designated community. Taking inspiration from my case studies of the Apete Villages of Brazil, the Camera Botanica of Ian Weir, and the different plants of British Columbia that engage with fire, I developed a six stage process to create a settlement in a fire-prone region of British Columbia. Utilising a combination of quick deployment structures and prefabricated “pods”, a new settlement can take root in wildfire areas as soon as the fires die out. This allows the community to aid in the regrowth of the ecosystem, and provide potentially displaced survivors with new housing quickly. As the community grows, fire management techniques are paired with housing additions to create a community that works together to protect the region, pairing regrowth of community with regrowth of the individual.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| 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".