Approaches to Addressing Informal Settlement Problems: A Case Study of District 13 in Kabul, Afghanistan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Afghanistan witnessed rapid urbanization in recent decades due to the post-war recovery process. When the war ended in 2001 with the fall of Taliban regime, most Afghan refugees returned to urban areas of Afghanistan, especially in Kabul. Moreover, the rapid urbanization, migration from rural areas, and population growth impacted Kabul with the manifestation of informal settlement. The residents of informal settlements suffer social and economic exclusion from the benefits and opportunities of an urban environment. Furthermore, the residents of informal settlements experience disadvantages such as geographical marginalization, shortage of basic infrastructure, improper governance framework, vulnerability to the effect of poor environment, and natural disasters. With all the above, the problems of informal settlements are considered enormous challenges for informal residents. Therefore, this paper aims to identify the proper approaches to addressing informal settlement problems in District 13 of Kabul. To reach the aim of the research, the interview and questionnaires survey were used as instrument in data collection. The finding of this paper indicates that through the resident’s preferences, government capacity, and District 13 physical condition, there are three approaches that can be implemented and adopted for improvement of informal settlement in District 13 of Kabul, which is settlement upgrading, the land readjustment, and urban redevelopment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it