The Cost of Inaction: A Portrait of Street Beggars in Medan City
Bibliographic record
Abstract
Poverty remains a pervasive problem in many cities, resulting in a large population of homeless people and beggars.This study examines the management of street beggars in Medan City, Indonesia, and finds that the current approach has not been effective in addressing the issue.Despite the existence of a regional regulation prohibiting homelessness and begging, the practice continues to persist, especially among children.The cost of managing street beggars is still relatively low, and the services provided are inadequate to meet their needs.Moreover, the local government's response is limited to appeals and temporary detention, rather than longterm solutions.The study used a mixed-methods approach, combining qualitative interviews and quantitative surveys of street beggars and government officials.The data revealed a complex picture of the challenges and opportunities in managing street begging, including the need for more comprehensive and coordinated services for homeless people and beggars, as well as greater public awareness and involvement in the issue.The findings suggest that a more holistic and inclusive approach is needed to address the root causes of poverty and homelessness, and to provide a sustainable solution for street beggars in Medan City.
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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 | 0.000 | 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 | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".