Driving Engines Effect on Settlement Patterns and Efficiency of the Settlement Influence at Al-Za’atari Camp
Bibliographic record
Abstract
As refugees lose their sense of spatial identity, they try to adapt by recreating their lost community through revival of rituals, religion, defense, lifestyle, prestige, and climate. Population displacement theory deals with settlements as form of socio-cultural action. The study the driving engines behind the shifted emerging patterns and their influence on the efficiency of the settlement at Al-Za'atari Syrian Refugee Camp, in North Jordan, Al-Mafraq. Face-to-face interviews with camp mobilizers explored the driving engines behind the shifted settlement patterns, and its influence on mobilizers' reachability. A cluster stratified random sample was used to collect quantitative data through a structured questionnaire. Outcomes indicated that refugees are gradually transforming the formal public spaces at the cluster level to private ones as an extension to the shelter domain. Such spatial shifts appear to be driven by a combination of physical, social, socio-spatial drivers, and past socio-spatial experience. These spatial shifts from the formal grid are influenced by refugees’ social values and territorial behavior, expressing zones of influence as means of defensive adaptation. Statistical analysis attested the influence of driving engines on settlement patterns and on the efficiency of the settlement. The driving engines behind the spatial shifts are safety concerns, cultural concerns, religious reasons, lifestyle, prestige, ethnicity and origin, improved infrastructure, improved access to services, and micro-climate. Such attributes influence the total efficiency of the settlement. Conclusively, planners should consider socio-cultural values that reflect defensibility, boundaries definition, and interdependence.
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".