Rest in Green: Exploring the Sustainability of Natural Burials
Bibliographic record
Abstract
Natural burials, often referred to as green burials, aim to provide a sustainable option for the internment of the dead. Human populations are continuing to grow and seeking a sustainable alternative to traditional death practices is needed to address socioecological sustainability. As a novel topic natural burials lack analysis through a sustainability framework to validate current academic research. This thesis examines natural burials from a Strategic Sustainable Development (SSD) perspective to examine their ability to address to the sustainability challenge. Natural Burial Organizations (NBO’s) and natural burial sites in Canada and the UK were used to identify processes unique to natural burials and their identifying characteristics. These processes were analysed through the eight sustainability principles, to determine alignments and misalignments to sustainability. The Sustainability Principles, SPs, make up boundaries and conditions that define what is needed for socio and ecological sustainability. The eight Sustainability Principles include three ecological principles; preventing systematic increases in nature through substances extracted from the Earth’s crust, concentrations of substances produced by society, and degradation of nature by physical means, and five social principles; health, influence, competence, impartiality, and meaning-making. Three types of natural burial sites, hybrid, natural, and conservation sites were also examined to determine how different natural burial sites impact their sustainability. Findings suggest that natural burials focus on ecological sustainability with social sustainability indirectly affected. While natural burials do not remove all barriers to sustainability, they create a less unsustainable death practice.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".