Rebuilding post-blast Beirut: nonprofit urban governance, sectarian moralities, and emerging commons
Why this work is in the frame
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Bibliographic record
Abstract
This article analyzes the urban governance of Beirut's post-blast reconstruction through a moral political-economy and urban-commons lens. Drawing on a mixed-methods dataset—a desk review of 220 organizations, a survey of 95 NGOs/FBOs, two-year participant observation, and nine key-informant interviews—it maps the actors who stepped into the vacuum left by an incapacitated Lebanese state. Network analysis reveals two loose coalitions: a Beirut-centered bloc of national nonprofits and university labs, with a claimed non-sectarian leaning, and a more atomized constellation of faith-based and sectarian groups. While both delivered relief, about 30 per cent of actors invested in collective goods such as public-space upgrades and heritage rehabilitation, signaling an aspiration to urban commoning. Three vignettes—Laziza Park, Nation Station and the Rmeil Cluster—show how these experiments collide with sectarian moral topographies, aid-industry imaginaries and real-estate interests. Laziza and Rmeil were re-enclosed; only Nation Station endures, its soupkitchen model aligning with neighborhood moral topographies. The findings extend debates on humanitarian urbanism: NGOs and aid agencies are not external substitutes but integral nodes in a densely networked, multi-scalar governance urban ecology that actively produces space. Yet their commoning projects rarely unsettle entrenched power; they remain circumscribed by political-sectarian, class and moral boundaries. By foregrounding the interplay of hybrid urban governance and moral topographies, the article argues that aid-driven and nonprofit-led urban commoning can widen democratic practice only when it forges coalitions capable of negotiating both political authority and market logics. Beirut's experience thus offers a cautionary insight for cities where humanitarian and nonprofit actors now lead urban interventions.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it