Green Afterlives: Green Burial as an Environmental Land Use in Settler-Colonial British Columbia
Bibliographic record
Abstract
“Green burial” is a form of death care in which cemeteries are made to restore a “natural” ecosystem, rather than a lawn-style landscape. While it has grown in popularity in recent years, the implications of green burial as a philosophy and a land use have not been explored in the British Columbian context. To address this gap, I brought together anthropological death scholarship with terror management theory, which locates a key cause of anthropocentric behaviour in a suppressed fear and subsequent denial of death, and applied these frameworks to four case studies of BC’s green burial grounds. I pursued three research questions. (1) Does green burial encourage an ecological view of the self? I found that green burial can promote the idea that humans are “part of nature,” but can still leave some anthropocentric assumptions intact. (2) Is green burial a death denying practice? While I saw that natural restoration in cemeteries can disguise the presence of death, such death denial serves, rather than harms, non-human beings. (3) What are the implications of green burial as a land use within the settler colonial context of BC? I found that in settler-run graveyards a “return to nature” can reinforce, rather than unsettle, the erasure of Indigenous people and traditions from this land. Ultimately, I found that green burial has an important role in alleviating the damaging environmental effects of conventional burial practices. However, a closer look at how green burial is actually being practiced in BC serves to complicate the often uncritical narratives present in the literature, and expose the tension that comes from treating damaging settler-nature relationships as a problem that can be solved in isolation from decolonial movements.
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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 | 0.001 | 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 | 0.019 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".