An empirical investigation of socio-economic impacts of agglomeration economies in major cities of Punjab, Pakistan
Bibliographic record
Abstract
Agglomeration economies are the external benefits earned from clustering of industries and people in cities. The study assumes unbridled clustering of population in emerging urban agglomerations turning economies into diseconomies. This study empirically investigates the heterogeneous socioeconomic impacts of agglomeration economies in selected cities of Punjab, Pakistan, from 1998 to 2018, using the Pooled Mean Group and the Mean Group techniques of Panel ARDL. Agglomeration economies are determined by population density, number of registered factories, employment size, and housing, in the cities of Punjab. The study designed four indices for socioeconomic conditions using principal component analysis. These include: education-index, healthcare-index, water & sanitation-index, and economic conditions-index. Research findings reveal pressures of high population density, unemployment, and costly housing on educational & healthcare facilities, poor sanitation & waste management, in cities of Punjab, Pakistan. The study suggests that policy makers and urban planners to develop short term and long term policies and development plans for villages and secondary cities to uplift wellbeing of the local population. Nonetheless, cities need to decentralize for sustainable development and management.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".