Bibliographic record
Abstract
Abstract On August 4, 2020, one of the largest nonnuclear explosions in history rocked the city of Beirut, leaving over 200 people dead, 6,500 injured, and 300,000 homeless. As Beirutis debated strategies for rebuilding, another reconstruction project haunted their discussions: the postwar reconstruction of Beirut's city center, overseen by the private company Solidere. For many, Solidere stands for a neoliberal reconstruction project that placed profits over people, expropriating rights‐holders for company shares. While most organizing against Solidere in the 1990s failed, this article turns to a little‐known but more‐successful mobilization involving Solidere's expropriation of parcels and buildings that had been endowed as inalienable Islamic trusts. Many Beiruti Muslims rallied against these expropriations, citing endowment inalienability and resulting in publicized recuperations. Yet, archival and ethnographic research shows that this mobilization led to the recuperation of only some of these endowed properties. Many others were taken to market in order to allow for the perpetuation of the few. This chapter in the reconstruction of Beirut's city center complicates our understanding of capitalism, alienability, and inalienability, illustrating both the constant work needed to create free markets and the continued effectiveness of discourses of inalienability that perpetuate other forms of value and property under capitalism.
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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 | 0.014 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".