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Record W4407702575 · doi:10.1016/j.cities.2025.105792

Cemeteries as critical social infrastructure: A comparative legal geography

2025· article· en· W4407702575 on OpenAlexaffabout
J. Page, Laurel Besco

Bibliographic record

VenueCities · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutions3v Geomatics (Canada)
Fundersnot available
KeywordsCritical infrastructureSocial geographyEconomic geographyGeographyCritical geographyPolitical scienceRegional scienceHuman geographyCultural geographyLaw

Abstract

fetched live from OpenAlex

Spatial shortages are jeopardizing the sustainability of cemeteries in urban communities, and to date, few studies have looked at this issue from a comparative lens. Examining cemetery law and policy in Toronto, Ontario, Canada and Sydney, New South Wales, Australia, this paper looks at how these jurisdictions are ignoring or addressing this issue and whether there are opportunities for learning. Using the lens of critical social infrastructure and the novel hybrid methodology of comparative legal geography, the research details the legal frameworks concerning cemeteries in order to understand the roots of the issue and examines whether spatial shortages have prompted government intervention. Although both jurisdictions rely on divergent planning approaches and only one, New South Wales, frames cemeteries within the lens of critical social infrastructure, our findings reveal that the provisioning of cemeteries and the costs of interment have made death an equity issue across urban communities. • The urgency to address spatial shortages relates to whether cemeteries are valued as critical infrastructure. • The regulatory relationships established in 19th Century legislation continue to impact cemetery sustainability. • Unaffordability means the scale of the problem spreads and becomes costly for governments both locally and regionally. • Political resistance to change is a clear obstacle for minority faith communities in the establishment of new cemeteries.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.335
Teacher spread0.318 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations3
Published2025
Admission routes2
Has abstractyes

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