The Consequences of the Fragmentation and the Division of Residential Units: A Case Study of Ramadi City in Iraq
Bibliographic record
Abstract
This research addresses a significant phenomenon that has become evident in many urban centers, namely the fragmentation of land plots and residential units, and the subdivision of parts of them into smaller uses that differ in shape and detail.This phenomenon has resulted in both negative and, in some cases, positive impacts.The study aims to examine the urban, organizational, economic, and social impacts resulting from this phenomenon.It is based on a comprehensive field survey methodology and the distribution of a questionnaire form, employing geographic statistical methods supported by library sources.The questionnaire was distributed to a 3% sample (227 completed questionnaires) of the total 3,772 cases experiencing fragmentation.This situation generated a substantial amount of data, as presented in the accompanying tables.The study concluded with several findings, the most prominent being that the phenomenon of fragmenting and subdividing residential units has left significant urban impacts, starting with the introduction of new building materials, modifications, vertical extensions, and a notable disparity in the city's skyline along main and local street facades within the same neighborhood or even on the same street.For example, a 4-meter skyline line appears adjacent to another that reaches more than 25 meters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".