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Record W3209391275 · doi:10.32920/ryerson.14661048.v1

Evaluating land use issues in places of worship: a case of Hindu temples in the GTA

2021· preprint· en· W3209391275 on OpenAlexaffabout
Rajaram Lamichhane

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in Asia
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsHinduismWorshipZoningFaithSociologyLawEnvironmental ethicsGeographyPolitical scienceReligious studiesTheology

Abstract

fetched live from OpenAlex

In Canada, every citizen has the right to pursue their cultural and religious heritage. Temples in the GTA are becoming an ideal place for Hindu communities to express their values in a foreign environment. As a result, they serve a vital function for the community on many levels, including psychological, social, and symbolic. This paper explores how Hindu faith group deal with land use planning issues related to locational constraints and zoning bylaws provisions when they establish temples in their communities. The research is based on a literature analysis of various scholarly works on places of worship and land use provisions as well as views and experiences of members from temple organizations and planners from the City of Toronto on the development process of temple construction. In addition, the paper includes case studies of two Hindu temples in the GTA and looks at the legislative and policy frameworks for places of worship. The research aims to underline the importance of temples in broader social undertakings, such as preserving the Hindu religion and culture for future generations. These research findings provide suggestions for a process for locating temples that meets the needs of both Hindu communities and other residents, and minimizes conflicts related to land use designations such as employment lands.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.487
Threshold uncertainty score0.829

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.237
GPT teacher head0.465
Teacher spread0.228 · 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 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

Citations0
Published2021
Admission routes2
Has abstractyes

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