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Record W4402896193 · doi:10.1515/9783839468265-006

The Capital of Closed Churches

2024· book-chapter· en· W4402896193 on OpenAlexaboutno aff
Hillary Kaell

Bibliographic record

Venuetranscript Verlag eBooks · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessEconomics

Abstract

fetched live from OpenAlex

Across North America, historic churches are rapidly closing.The problem is especially acute in urban areas where these buildings often house community organizations.Graham Singh, an Anglican pastor and non-profit CEO in Montreal, is promoting a solution: remake churches into community hubs.For Singh and his team, hubs are an opportunity for Christians to leverage their primary asset -tax-free land -and become full partners in the public sphere.Based on anthropological fieldwork, this chapter argues, first, that more scholarship should consider social entrepreneurship as a key area where religion and market meet, beyond much-studied neo-Pentecostal growth churches and prosperity gospel.Instead, Singh and his team are working to define entrepreneurship as social by dint of its physical embeddedness in historic churches.Doing so, they consciously adapt cutting-edge financial trends by positioning hubs as a smart real estate investment for private investors with social purpose goals.In this view, church property, supported by private investment, becomes central to reinvigorating Christian influence in the public sphere.In keeping with the theme of this volume, this chapter's second contribution is to suggest that community hubs might therefore be considered an intriguing new social form within North American Christianity, which derives value from its location at the border of historically religious forms (heritage churches), economic forms (corporate investment), and the public sphere.

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)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.682
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.028
GPT teacher head0.264
Teacher spread0.236 · 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
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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