Beyond burial: researching and managing cemeteries as urban green spaces, with examples from Canada
Bibliographic record
Abstract
Cemeteries existing within cities are often omitted from the “green space” narrative despite their high levels of vegetation. Given the relatively small areas of green space in many cities, it is important to appropriately manage these landscapes to ensure that residents can access green spaces and enjoy the many benefits they offer. The purpose of our paper is to demonstrate that cemeteries should be managed and researched as urban green spaces that provide ecosystem services. We compared and contrasted cemeteries with urban parks and used their similarities and differences, as well as a review of existing cemetery and other green-space literature, to discuss how cemeteries can provide a wide variety of ecosystem services. We found that cemeteries and parks both have high levels of vegetation, similar perceived safety issues, and some common rules and regulations, while differing in their consideration as public spaces, effect on real-estate values, historical–cultural value, and funding and management goals. Despite the differences, we believe that the vegetation (particularly trees), monuments, other infrastructure, and atmosphere within cemeteries make them well-suited to providing ecosystem services such as recreation, human health and restoration, stormwater management, microclimate regulation, aesthetics, and so on. Cemeteries can also potentially provide ecosystem “disservices” such as allergens, invasive/dangerous/poisonous species, and the degradation of groundwater quality. However, we believe that the potential for ecosystem services far outweighs the potential for ecosystem disservices in urban cemeteries, and as such we believe they should be studied and managed as green spaces with functions beyond those of interment and mourning. Given the general superiority of trees over other vegetation in providing a diversity of ecosystem services, we urge cemetery managers to consider options for increases and improvements in cemetery tree populations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".