Unplanned links, unanticipated outcomes: Urban refugees in Halba (Lebanon)
Bibliographic record
Abstract
Much of the literature covering forced population displacement has neglected spatial implications, dealing with place as mere context. Building on a case study of the city of Halba (Lebanon) where it maps a process of contingent encounters through which disparate resources, individuals, and groups are stitched together to generate large-scale housing projects that shelter refugees, this paper demonstrates the importance of studying displacement through a grounded reading of the spatial transformations it implicates. The paper maps multiple private and public actors who exploit cracks and connect seemingly disparate material flows (e.g., humanitarian aid, public housing subsidies) and institutional systems (e.g., humanitarian, public, private, religious) in ways that catalyze and accelerate the production of housing in order to derive profit from the opportunities afforded by the refugee crisis. Thus, developers, buyers, residents, brokers, public agents, and other actors all support a flow of informal profitable exchanges that are far from seamless economic transactions, mixing instead capitalist profiteering with human solidarity, religious morality, or kinship obligations that all buttress the possibility of these encounters and their materialization into new configurations of urban quarters. The arrangements formed through these processes, the paper shows, are strongly reflective of existing social hierarchies and inequalities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".