Impact of Migration Resulting from Ethnic and Racial Armed Conflicts on Accelerating Urban Sprawl
Bibliographic record
Abstract
After 2003 (as a result of the absence of state authority and the spread of anarchy and post-war military operations), the urban sprawl has increased significantly in the Baghdad city, after that, terrorist acts and the period of the civil war started (2006-2009), this has exacerbated the problem and led to the emergence of population settlements. Especially The outskirts of Baghdad with a low level of urban, economic, educational, and cultural levels., It caused an increase in the size of the problem, reaching 92% during a study prepared in 2013. Thus, the research problem is determined: The lack of a clear vision of the impact of the civil war in Iraq (2006-2017) on accelerating the urban Sprawl in Baghdad. One of the most important findings of the research is that the rate of growth of Urban Sprawl that started in 1958 has maintained a near-constant rate until 2009, which witnessed an unprecedented acceleration and reached its peak in 2017. The essential research conclusion and recommendation can be summarized that the impact of civil wars on the exacerbation of the urban sprawl phenomenon differs radically from that of conventional wars in terms of quantity and quality, and it needs planning and social solutions that differ significantly from the type of solutions that were previously adopted to reduce the problem.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".