Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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".