Grave concerns: capturing religious diversity in cemetery planning
Bibliographic record
Abstract
It has been estimated that the remaining cemetery land in Toronto will run out of space within the next 30 years. Although death is the only certainty we have in life, planners aren’t planning for it. Toronto’s population is increasingly aging, growing, and diversifying, which makes this an issue that can longer be ignored. There are 23 active cemeteries in Toronto, of which only 13 are non-denominational cemeteries that are able to capture the religious diversity for accommodating the deceased. Through this paper, it is found that cemeteries not only provide an essential public service, but they also play an important role in anchoring immigrant communities. Through exploratory research methods, findings suggest that those religions that require in-ground burial will face the brunt of accessing affordable cemetery services in Toronto. Recommendations are made to address this land use policy gap and calls for action to increase supply within existing cemetery lands in Toronto so that cities are not only planned for the living, but also for the dead. Key words: cemetery; diversity, religion; immigration; land use; death sprawl, Toronto
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".