Developing a Methodological Model for Monitoring and Measuring Urban Poverty and Deprivation, and Its Spatial Distributions
Bibliographic record
Abstract
In recent decades, the issue of urban poverty and deprivation become one of the most imperative problems experienced by countries at global level from the economic, political, social, and urban interaction processes, as World Bank estimated nearly 700 million persons fall below extreme poverty line in 2017.However, most studies have focused on exposure to identifying phenomenon and measuring its various elements and monitoring variables affecting it, without focusing on its spatial distributions and disparities within cities -to build revealing maps to measure disparities that raise the efficiency of planning and making urban development policies.This study aims to fill the current research gap by improving monitoring and measurement processes and developing flexible building model multidimensional.Therefore, research is related to how to formulate a methodological model for measuring urban poverty and deprivation stemming from a multiplicity of theoretical and applied approaches, diagnosis of objectives and indicators affecting it.The results showed novelty of this research is to reach development of a methodological model to control measurement and raise the efficiency of analysis and identification spatial distribution and hotspots in inner city to enhance efficiency of planning and sustainable urban development policies.
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.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".