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Record W4248525210 · doi:10.32920/ryerson.14660283

An Analysis Of Land Use Planning Policies For Cemeteries In Ontario

2021· preprint· en· W4248525210 on OpenAlexaboutno aff
Michael T. Larkin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicHistorical and Cultural Archaeology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental planningLand useLand-use planningGeographyPopulationPolicy developmentRegional sciencePolitical sciencePublic administrationCivil engineeringEngineeringSociology

Abstract

fetched live from OpenAlex

Cemeteries are important to society and represent a key piece of the fabric of municipalities. In the Province of Ontario, land use policy as articulated through official plans often fails to recognize cemetaries as a necessary element of municipalities. This paper examines the official plans of selected municipalities to ascertain the extent to which appropriate land use policies are provided to guide the development of cemetaries. Official plans are reviewed for the ten largest municipalities as determined by their population, all adjacent municipalities, and all associated regional municipalities of counties. In total, the official plans of forty-six municipalities are reviewed. The analysis focuses on eight key policy criteria identified in this paper that relate to cemetary development: need, planning horizon, location, size, intensification, compatibility, environment, and permanency. The review confirms the hypothesis that there is a general lack of appropriate land use policy necessary to guide cemetery development in Ontario.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.507

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.011
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.124
GPT teacher head0.357
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2021
Admission routes1
Has abstractyes

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