Bibliographic record
Abstract
The number of inhabitants of urban areas is rising rapidly, leading positive, together with every day more significant negative aspects of urban development becoming visible in all countries of the world. Responsible and sustainable management of urban development should therefore be set as one of the priorities, in order to maintain cities to be centers of opportunities and innovations as much as possible, and less the origin of problems such as social inequalities, urban poverty, economic and environmental constraints. The paper describes the notion of urban poverty, different concepts and perceptions of poverty are presented, methods (especially limitiations) of measurements and trends of poverty are compared, the consequences of living in such conditions and examples of possible measures at the national and local level for combating poverty. Subsequently, the analysis focused on urban poverty in developed countries, considering the smaller number of studies of the observed problem in the prominent group of countries. It has been proven that urban poverty is present in each of the observed developed countries as a measurable and realistic category (The United States, Canada, Italy and other selected European countries). However, it is important to emphasize that the analysis also identifies significant problems of poverty in out-of-town and rural areas, which have been compared in detail on a separate examples. Conclusions suggests that the poverty of each individual country is a reflection of the current state of affairs related to specific characteristics (historical, demographic, social or political), the result of heterogeneous problems specific to each country, and which should be included in appropriate policies aimed at resolving urban poverty. The analysis of the problem of urban poverty requires a clear definition of the appropriate approach in the measurement so that the results, and then the resolution instruments, are effectively set up / implemented.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".