Climate storytelling in Lytton, B.C., Canada: Salvaging archives and cultural collections in the burn zone
Bibliographic record
Abstract
In 2021, a heatwave-fuelled wildfire burned through much of the town center of Lytton, British Columbia and large portions of neighbouring First Nations reserves, destroying homes, government offices, and businesses, as well as four significant cultural collections. In the wake of the fire, news media and government officials alike were quick to cast the event as a climate change phenomenon. These official understandings of the fire shaped policy responses, which imagined the future of Lytton as a net-zero, climate-resilient model community. For the affected communities, however, this framing of climate change as the cause of the fire obscured other causes—like the possible role of the train in sparking the fire—and local cultural and environmental histories that shape the region's relationship and vulnerability to fire. In a case study of the loss and recovery of the Lytton Chinese History Museum, this essay argues that local archival and cultural collections—even, and especially, when they are lost or damaged from environmental phenomena—provide a framework for climate storytelling built on the intersections between local environmental and cultural histories and global environmental transformations. Building on interviews and site visits with local cultural stewards, knowledge keepers, community members, and conservation professionals undertaken in the production of an audio documentary series titled Archival Ecologies , this article articulates an interdisciplinary methodology for post-disaster archival work and climate storytelling. Connecting cultural collections with their communities and geographies, this essay contextualizes archives in terms of their environments and formulates an approach to reading cultural collections when they have suffered environmental damage. Such collections offer pathways to nuanced, community-centered, historically informed climate stories and to the recovery of communities with complex histories.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.039 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".